source-library

RACIALISM AND RACE REALISM

General

General

Race is an arbitrary social category: there is more differentiation among members of a race than between races. This is backed up by a consensus among anthropology researchers and experts.

Links:

• r/badscience

• https://rationalwiki.org/wiki/Racialism

• Debunking the Lynn and Vanhanen study from 2006 (BROKEN LINK)

• Useful links for arguing with racists

• Sources and Studies to Debunk Racism and “Race Realism” : BreadTube

• Complexity, Genetic Causation, and Hereditarianism (Alternative approach to hereditarianism)

• This topic matters in regards to the spread of white supremacy (see: Panofsky et al. 2018) and how information is spread (Carlson & Harris 2020)

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Race Classification

Race Classification

• Kaplan 11
  • Prisoners of Abstraction? The Theory and Measure of Genetic Variation, and the Very Concept of ‘‘Race’’
  • (objective constructionism)
• Yu et al. 02
  • There are larger genetic differences within african populations than between africans and eurasians
  • In other words, race does not indicate significant genetic differences and is only phenotypic in nature.
• Hahn & Stroup 94
  • “Membership in different racial and ethnic groups also bears important social and symbolic meaning. For these reasons, the determination of racial or ethnic membership is not a simple matter of measurement. Because scientific use of a social category may be interpreted as endorsement of its validity, the use of these categories is not only a matter of scientific method, but also of policy and ethics“ (Hahn & Stroup [pg. 13]).
  • Race impacts other areas than simply measurement
• Marks 10 (pg. 270)
  • Responds to A.W. Edwards’ “Human Genetic Diversity: Lewontin’s Fallacy”
  • “What is unclear is what this has to do with ‘race’ as that term has been used through much in the twentieth century—the mere fact that we can find groups to be different and can reliably allot people to them is trivial. Again, the point of the theory of race was to discover large clusters of people that are principally homogeneous within and heterogeneous between, contrasting groups. Lewontin’s analysis shows that such groups do not exist in the human species, and Edwards’ critique does not contradict that interpretation.”
• Norton et al. 2019
  • Human races are not like dog breeds: refuting a racist analogy
  • Also responds to Bamshad et al. 2003
• Witherspoon et al. 07
  • Further explores human genetic similarities between and within races.
  • Prompt: “How often is a pair of individuals from one population genetically more dissimilar than two individuals chosen from two different populations?”.
  • Analyzed three distinct populations: European, African, and East Asian
  • After analyzing the genetic similarity of 1000s of loci, they came to the conclusion that never was the answer (alt-right will spam this answer, but will pay no attention to the following)
  • “On the other hand, if the entire world population were analyzed, the inclusion of many closely related and admixed populations would increase. This is illustrated by the fact that Formula and the classification error rates, CC and CT, all remain greater than zero when such populations are analyzed, despite the use of >10,000 polymorphisms (Table 1, microarray data set; Figure 2D).”
  • However, measuring similarity using smaller numbers of loci yielded substantial overlap between these populations. Rates of between-population similarity also increased when geographically intermediate and admixed populations were included in the analysis.
  • Research comes to the same conclusion: “Most human genetic variation is found within populations, not between them”
  • Can be used as a critique of Edward’s “Human Genetic Diversity: Lewontin’s Fallacy”
• Harvard: Chou 17
  • A further look at what studies say about genetic variation
  • Stanford 02
    • Only 7.4% of over 4000 alleles were specific to one geographical region
    • Even when region-specific alleles did appear, they only occurred in about 1% of the people from that region—hardly enough to be any kind of trademark
  • Stanford
    • Over 92% of alleles were found in two or more regions, and almost half of the alleles studied were present in all seven major geographical regions.
    • The observation that the vast majority of the alleles were shared over multiple regions, or even throughout the entire world, points to the fundamental similarity of all people around the world—an idea that has been supported by many other studies
• Wagner et al. 12
  • Assessment of anthropological experts on race
  • The study finds a broad consensus in the field of anthropology that race is a social category, not a biologically significant one.
  • One might be confused: How can anthropological experts claim race is a social category yet still derive one’s race from something like skulls? The answer lies in the simple explanation that what is actually being done is identifying that some particular specimen was assigned some socially constructed race at that time (Ex. a particular specimen might have features tied withAfrican ancestry. To assign a particular race to this person is not support for racial categorization. See here and here)
• American Society of Human Genetics 18
  • A look into the academic consensus on race in the field of genetics
  • “Genetics demonstrates that humans cannot be divided into biologically distinct subcategories.”
  • “Most human genetic variation is distributed as a gradient, so distinct boundaries between population groups cannot be accurately assigned.”
• Guido et al. 01
  • “Zones of discontinuity in human gene frequency distributions are present, but the local gradients are so small that they can be identified only by simultaneously studying many loci using complex statistical techniques. In addition, such regions of relatively sharp genetic change do not surround large clusters of populations, on a continental or nearly continental scale. On the contrary, they occur irregularly, within continents and even within single countries”
• Guido & Colonna 11
  • Updated version of previous source
  • “The genetic exchanges occurred in the course of the frequent contacts have resulted in a smooth, continuous variation of many genetic parameters. As a consequence, zones of sharp genetic change are not the rule, but the exception; most human populations are not surrounded by clear genetic boundaries.”
• Maglo et al. 16
  • The genomic and statistical evidence currently available shows that phylogenetic and genetic similarity-based concepts of race fail to be applicable to humans even under minimal rational theoretical principles currently accepted in population genetics/genomics
• Romualdi et al. 02
  • These results suggest that, at random biallelic loci, there is little evidence, if any, of a clear subdivision of humans into biologically defined groups
• Serre et al. 04
  • Our results show that when individuals are sampled homogeneously from around the globe, the pattern seen is one of gradients of allele frequencies that extend over the entire world, rather than discrete clusters. Therefore, there is no reason to assume that major genetic discontinuities exist between different continents or “races”
• ASHG 18
  • It follows that there can be no genetics-based support for claiming one group as superior to another. Although a person’s genetics influences their phenotypic characteristics, and self-identified race might be influenced by physical appearance, race itself is a social construct. Any attempt to use genetics to rank populations demonstrates a fundamental misunderstanding of genetics
• Smay & Armelagos 2000
  • “[T]he 85 percent to 90 percent results are based on the accuracy of the method when standards are developed on a subset of a sample and then “tested” on the sample from which the subset was derived. For example, in the 1960s when Eugene Giles and Orville S. Elliot (1962) developed their formula to determine race from the crania, they used a sample that was a sub-set of modern adult Blacks, modern adult Whites and Native American skulls from an archeological site (Goodman 1997b). They applied a statistical procedure—discriminant function that separates crania into “races” using eight measurements. When they applied the formula to the rest of the crania in the same sample, they achieved the much touted 85 percent to 90 percent accuracy. When applied to other samples of Blacks, Whites and Native Americans, they achieved 18.2 percent and 14.3 percent accuracy, figures that hardly instill confidence (Goodman 1997b).”
  • This 85-90% estimate coming from foreign Anthropologists who claimed race could be determined from phrenology alone based on those estimates

Format and look through:

• Evidence for Gradients of Human Genetic Diversity Within and Among Continents (cshlp.org)

• Global patterns of isolation by distance based on genetic and morphological data (nih.gov)

• Going the Distance: Human Population Genetics in a Clinal World (wsu.edu)

• Clines Without Classes: How to Make Sense of Human Variation (wisc.edu)

Rebuttals:

• It is unclear why studies like Rosenberg et al. 2002, Tang et al. 2005, and Guo et al. 2013 are cited as some sort of refutation of social constructivism. Even if the concept of race were not “popped” (note, regardless of which theory of race is preferred, the racialist concept of race should absolutely be “popped”) as described by Hochman, the utility of minimalist races (as described by Hardimon) whereby supporters reject the psychological aspect of race differences in favor of physical differences renders every one of these studies useless for hereditarians seeking to defend the assumed racial categories. Therefore, tying geographic ancestry to ethnic or racial groups does not in any way refute social constructivism.
  • Guo et al. 2013 has its own set of problems. See here
  • Tang et al. 2005 has conflicting evidence in health. See here and here
  • Rosenberg et al. 2005 is often misinterpreted by hereditarians. See Mills 2017 and his discussion of other authors like Wade and Sesardic. Using STRUCTURE to then say that some genetically distinct population is a race is confusing considering when Rosenberg set K=6, the Kalash people were highlighted. More troubling are the results of Tishkoff in 2009 where they find 3 distinct African groups, Caucasians, and Mongoloids (Oversampling will likely be brought up when mentioning this study but this does not reject the main argument). The question to hereditarians after this argument is as follows: what exactly is the criteria for racehood? Jensen and Rushton simply assumed the existence of race with no definition whatsoever.
• West African Sprinters as evidence of meaningful racial categorization?
  • Often posited is the claim that racial differences are represented in sports such as in the Olympics. For example, most men of the Olympics 100M have been males of West Arica origin. This claim is, however, irrelevant as we would then expect those with the “most West African ancestry” to do best on these events. However, this is not shown from actual data: see here

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Nature vs Nurture

Nature vs Nurture

• Bird 20
  • Respond to two major claims by proponents of the genetic hypothesis:
  • that substantial genetic differences between Black and white populations exist, and which cause the observed gap in cognitive ability and academic achievement
  • that genetic differences associated with cognitive ability are the result of divergent natural selection.
  • “Results presented here indicate that known biases from population structure, assortative mating, indirect genetic effects, gene-environment interplay, and derived allele frequency differences between African and non-African populations bias polygenic score analysis.”
  • Shows:
    • “that these biases likely produced false signals of polygenic selection in recent analyses, and that there are no signals of divergent polygenic selection between African and European populations.”
    • “I further show the predicted genetic contribution to the Black-white gap in IQ score across a range of heritability estimates was substantially smaller than observed phenotypic gaps, suggesting at least 80% of the IQ variance between Africans and Europeans is environmental in nature under an idealized “best case scenario” for the hereditarian hypothesis.”

• Because of key violations of the model, the estimation for genetic contribution to IQ differences is likely even smaller than the value given. The claims for large, immutable group differences in intelligence and educational attainment are not supported in the least by these analyses.

• Kan 11
  • GxE impact on Intelligence
  • This impact leads to complications for the method of correlated vectors
  • Jensen’s effect is, thus, an invalid argument for the g-factor
  • “Nevertheless, a distinction between g as a statistically entity, and g as interpreted as a realistic, common cause of individual differences, is not only important theoretically, but also empirically. Consider genetic association and linkage studies of intelligence, for example. So far, the search for genes for general intelligence has met with relatively little success (Deary, Johnson, & Houlihan, 2009; Plomin and Spinath, 2004; Chabris et al., in press). The alternative theories of Dickens and Flynn (2001; Dickens, 2008) and of van der Maas et al. (2006), in which the general factor of intelligence is a statistical entity originating in reciprocal beneficial interactions among cognitive processes or abilities, are able to provide a plausible explanation of this lack of success.”
• Wicherts et al. 10
  • “Another failure to replicate Lynn’s estimate of the average IQ of sub-Saharan Africans” While Lynn finds the average IQ to be at 67 (compared to UK norms after a correlation of the Flynn effect) after their performance in Raven’s Progressive Matrices, they criticize his methods for being unsystematic. Once again, they fail to replicate low estimates.
  • “We argue that these scores are hard to interpret in terms of latent cognitive variables such as g because of the psychometric incomparability we established and because the Flynn Effect has yet to take hold in sub-Saharan Africa” (Wicherts et al. 10 [pg. 1]). They come to a difference of ten points between his estimate to theirs. The process by which they disproved Lynn’s estimate is shown: “(1) our use of systematic methods and a lack thereof in Lynn’s work; (2) our use of weighting by sample size to estimate the mean IQ across samples and Lynn’s indifference to sample sizes; (3) our decision not to include unhealthy samples, which Lynn admitted; (4) our exclusion of samples in which test administration had met with problems, which Lynn attributes to low cognitive ability of test-takers; (5) our exclusion of data from the Coloured Progressive Matrices (CPM) for ages above 11 because the conversion from CPM scores to adult and adolescent norms for the Standard Progressive Matrices (SPM) artificially lowers the IQ; (6) Lynn’s exclusion of a number of high-IQ samples that he deemed unrepresentative; and (7) Lynn’s ad hoc downward correction of mean IQs from primary and secondary school students by two and six IQ points, respectively” (Wicherts et al… 10 [pg. 1]).
• Wicherts et al. 08
  • A systematic literature review of the average IQ of sub-Saharan Africans Flynn’s effect negating the results of previous claims about Sub-Saharan African IQ
  • “Our estimate of average IQ converges with the finding that national IQs of sub-Saharan African countries as predicted from several international studies of student achievement are around 82. It is suggested that this estimate should be considered in light of the Flynn Effect.”
• Wicherts et al. 09
  • The dangers of unsystematic selection methods and the representativeness of 46 samples of African test-takers. A critique of Lynn’s process to account for Flynn effect and to estimate the average IQ (in terms of British norms after correction of the Flynn Effect) of the Black population of sub-Saharan Africa.
• Joshi et al. 15
  • In each case, increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months less education
• Thomas 17
  • Diagnosing these artifacts suggests a nil hypothesis: East Asian, White, and Black adoptees raised in the same environment would have similar IQs, hinting at a minimal role for genes in racial IQ differences
• WIP
  • https://www.nature.com/articles/nature02842
  • Rosenberg et al. 18

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Twin Studies and EEA

Twin Studies and EEA

• Moore & Shenk 16
  • Use value of Heritability, Twin Studies, Variation vs Causation.
  • Sections: 1. THE GROUP VS. INDIVIDUAL FLAW 2. THE ENVIRONMENTAL FLAW 3. THE BIOLOGICAL FLAW 4. WE INHERIT DEVELOPMENTAL RESOURCES, NOT TRAITS
  • Explains that since there exists only an interaction between genetics and environment, the usage of variation to make claims is limited
  • TWIN STUDIES: “The math of twin studies is based on the assumption that environments encountered by identical twins are no more similar than environments encountered by fraternal twins. However, the formulas are not sound if identical twins, in addition to having more genetic similarity, are also exposed to more environmental similarity; in that case, increased similarities in identical twins’ traits can just as easily be accounted for by environmental similarities as by genetic similarities.” (NOTE: corrections for EEA by determinists are useless as “Studies have, however, found demonstrable EEA effects13–16 that effectively invalidate the assumptions upon which heritability claims rely.”)
  • (citations for EEA) 13- Fosse et al. 15 , 14-Hettema et al. 95 , 15-Joseph 15, 16-Richardson & Norgate 05
  • (regarding twin studies): “while fraternal twin embryos are always connected to their mother via two unique placentas, identical twins most often (but not always) share a single placenta. This means that, beginning soon after conception, identical twins typically have more similar access to nutrients, oxygen, and other factors than do fraternal twins (Figure 3). But does this increased similarity matter? Yes. In fact, identical twins who share a placenta as fetuses have more similar IQs [here] and personalities [here] than do identical twins who did not share a placenta.”
  • IMPORTANCE: “Because genetic and nongenetic factors interact with one another to produce phenotypes, it is not possible to accurately assess the relative importance of their contributions to the phenotypes they produce.”
  • VARIATION VS CAUSATION: “Thus, focusing on variation rather than causation can contribute to a misleading sense of how important a particular factor might be in contributing to a particular outcome.” (Conclusion of the house fires example given in that section)
• Epigenetic similarity:
  • The finding that monozygotic twins have higher concordances for methylation, but that the frequency of contact among twins was correlated with concordance from Fraga 05 is in conjunction with other evidence. Also been found in varying types of cells, and most studies support epigenomic, environmental or developmental causes (Kaminsky et. al 2009, Poulsen et. al 2007, Wong et. al 2010, Ollikainen et. al 2010, Martino et. al 2013).
• Burt & Simons 15
  • Response to BARNES and WRIGHT
  • “We argued that heritability studies are futile for two reasons: 1) Heritability studies suffer from serious methodological flaws with the overall effect of making estimates inaccurate and biased toward inflated heritability and deflated shared environmental influences, and more importantly, 2) the conceptual biological model on which heritability studies depend—that of identifiably separate effects of genes versus the environment on phenotype variance—is unsound.”
• Van Baak et al. 18
  • found that MZ twin concordance on methylation was 2-16 times greater than DZ twin concordance, and found that this is very likely to be the result of a developmental process. They concluded that their findings could explain some of the missing heritability, as heritability of traits could be overestimated due to the violation of the equal environment assumption.
• Fosse et al. 15
  • Examined specific risk factors for mental ‘disorders’ and universally found that monozygotic twins experienced more similar environments than dizygotic twins.
• Prenatal Similarity as a flaw in EEA
  • It has become pretty clear that there are abundant maternal effects on all sorts of behavioral, physical and psychological traits (Coe et al. 2003; Champagne 2010a; Field et al. 2004a; 2004b; 2006; Gluckman & Hanson 2005; 2006; Horton 2005; Heijmans et al. 2008; Huizink et al. 2002; Kemme et al. 2007; Langley-Evans et al. 1999; Lui et al. 2011; Maccari et al. 2003 Oberlander et al. 2008; O’Connor et al. 2005; Painter et al. 2005; Parnpiansil et al. 2003; Ryan & Vandenbergh 2002; Sandman et al. 2011; Schneider et al. 1999; Waterland & Michels 2007; Welberg & Seckl 2001; Weinstock 2008; Tamashiro & Moran 2010; Thompson 1957 [1]). (from source below)
  • If there were to be a violation of the EEA in utero, this would constitute a serious blow to the idea that twin studies can inform us on the causes of human variation.
• The Equal Environment Assumption Is False
  • “The reason why the EEA is, in essence, an unverifiable assumption even when subject to purported ‘tests’ is because the list of environmental influences that could differ by zygosity is endless, since we know that there are numerous nonobvious environmental influences on behavioral development (Turvey & Sheya 2017, Wallman 1979).”
• Ho 13
  • Problems with measuring heritability
  • Problems with EEA
  • Flaws of Bell curve etc..
• Charney 12
  • Evan Charney at Duke University speaks of a “paradigm shift” in the science of genetics. He points to recent discoveries of numerous processes that create extensive mutations in genome sequences and structure, as well as epigenetic modifications, which are completely at odds with the Mendelian model of inheritance underpinning heritability estimates
• Joseph et al. 15
  • Response to Barnes et al.
  • Felson 2014 is often cited but Joseph reviews over it in comparison to other tests of EEA and find support for Joseph’s claims
• Joseph 20a
  • Response to studies testing EEA (Felson, Segal, etc…)
• Joseph 20b
  • Response to three books defending EEA

• Levels of Identity Confusion and Attachment among Reared-Together MZ and DZ Twin Pairs

• Jay Joseph 15
  • Response to Turkheimer on Twin studies

ADOPTION STUDIES

While adoption studies suffer fewer flaws than do Twin studies (Joseph 2013), there are still flaws to note.

• Richardson & Norgate (2006)
  • Adoption studies (specifically dealing with comparing the correlation of the adopted children to their biological parents and their adoptive parents) suffer from range restriction, selective placement, and family unit effects
  • Moreover, both forms of adoptee studies suffer from the issue of confounds from epigenetic inheritance, effects from the prenatal and pre-adoptive environment.
• van Ijzendoorn et al. 2005
  • International adoption studies provide another piece of converging evidence that rearing environment does have long-lasting impacts on cognitive function
  • Meta analysis that found average effect of adoption of 18 points when extremely deprived institutional settings were included in the comparison
• Leve et. al (2014)
  • report significant mean increases in achievement and IQ despite near-null correlations of adoptive parent and adoptees
• REBUTTAL TO: McGue et. al (2007)
  • Argument: Source purportedly demonstrates that not only does restriction of range not have a significant impact on the shared environmentality components estimated [1], but that socioeconomic status itself does not impact IQ.
  • The source’s estimates of SES are not good enough. the measures were only parental education and occupational status, both of which are known to be imperfect representations of socioeconomic status (American Psychological Association 2006; Lott 2012; Kraus & Tan 2015)
  • The second issue here is that their method of testing for variance reduction was to compare to the group of adoptive families in their sample to the group of ‘biological families’ in their sample..The sample of ‘biological families’ has a much smaller range of environments than that of the general population (Nisbett et. al 2012)
  • Also the regression coefficient of socioeconomic status on IQ in adoptive families is positive (page 458, Table 4), but the 95% confidence interval includes zero (likely due to the low power in the study & issues with indices).
  • Even then, extended familial studies have greater power to distinguish between confounded variance components (Rao et. al 1976), which universally show smaller estimates (Marcus & Feldman 2018).
  • McGue provides an explanation for reduction in variance, but see here

WILSON EFFECT

• Dickens & Protzko 15
  • the effect is non-linear and that the shared environment component may not reach zero in adulthood using twin methodologies
• Rao et. al (1982)
  • found that the heritability of IQ increased in adulthood for phenotypic homogamy, and decreases in adulthood for social homogamy (social homogamy seems to be the case for IQ, Keller et. al (2019)).
• Dickens 19
• Beam et al. 15
• De Kort et. al (2014)
• Briley & Tucker-Drob 13
• From Dickens 19 down: gene-environment interaction and gene-environment (evocative and active) correlation explanations of the increase.

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GWAS & PGS

GWAS & PGS

• Richardson & Jones 19
  • “Here we show how, in the context of CA and EA as approximation measures, spurious correlations in GWAS/PGS can arise in a number of ways, particularly from genetic population structure. We review recent studies suggesting that attempts to control for such confounds have been quite inadequate, and also criticize the underlying statistical assumptions and genetic model.”
  • [unrelated to section; more to do with performance] Finally, different social conditions also lead to different affective orientations, such as self-confidence and achievement expectancies, that impact on school learning and test performances (Frankenhuis & de Weerth, 2013; Odgers, 2015; Schmader, Johns, & Forbes, 2008). The effects of test anxiety on cognitive performance are well known, and have been estimated to affect up to 15%–20% of school children (Chin, Williams, Taylor, & Harvey, 2017). In addition, feelings of social rejection effect test performances and self-regulation (Stillman & Baumeister, 2013).
  • In sum, whatever else CA and EA scores measure, they at least partly reflect a socio-psychological population structure in ways probably unrelated to any general cognitive or learning ability.
• Meyer et al. 20
  • Response to Charles Murray on Polygenic Scores
  • Baverstock 19
    • I argue here that polygenic scores are a public health hazard because the underlying methodology, genome wide association, from which they are derived, incorrectly assumes that the information encoded in the genomic DNA sequence is causal in terms of the cellular phenotype
• Mostafavi et al. 20
  • Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design.
• Richardson Dec. 2019
  • Polygenic scores are an even bigger social hazard: Commentary on: Baverstock, K. (2019) polygenic scores: Are they a public health hazard? Progress in Biophysics and Molecular Biology.
• Kerminen et al. 19
  • Subtle population stratification in Finland induces spurious polygenetic differences in propensities for various complex traits (ex. MI, height and coronary artery disease)
• Martin et al. 17
  • We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable.
  • By use of polygenic score, “Africans” would be about less than 5 feet tall, indicating intercepts/levels of phenotypes can be misunderstood (see here for more)
• Durvasula & Lohmueller 2019
  • “Across these phenotypes, we find ~50% of the heritability comes 29 from European-specific variants, setting an upper bound on the accuracy of genetic risk prediction in non-European populations using effect sizes discovered in European populations.”
  • Eurocentric biases in GWAS (further see Coop 19 for gene-gene & gene-environment interactions as a cause for the misinterpretations stated above)

Rebuttals:

• Plomin & Stumm 18 for full see RR’s blog
  • Claim from above: “GPS are unique predictors in the behavioural sciences. They are an exception to the rule that correlations do not imply causation in the sense that there can be no backward causation when GPS are correlated with traits. That is, nothing in our brains, behaviour or environment changes inherited differences in DNA sequence. A related advantage of GPS as predictors is that they are exceptionally stable throughout the life span because they index inherited differences in DNA sequence. Although mutations can accrue in the cells used to obtain DNA, like any cells in the body these mutations would not be expected to change systematically the thousands of inherited SNPs that contribute to a GPS.”
  • Firstly view above sources on how the studies are skewed towards european populations and also that “scores derived from GWA studies which are associational and therefore cannot show causes” (see Martin et al. 17 above, Curtis 18, and Haworth et al. 18)
  • “PGSs are also carried out based on the assumption that the heritability estimates derived from twin/family/adoption studies tell us anything about how “genetic” a trait is. But, since the EEA is false (Joseph, 2014; Joseph et al, 2015) then we should outright reject any and all genetic interpretations of these kinds of studies.”
  • Further see how in the above citations of richardson that demonstrates how these studies show: the population structure of the population sampled in question as well as (Zaidi and Mathieson, 2020) finding that the demographic history of the sample in question can also mediate the stratification of the population
• Lee et al. 2018
  • Firstly, there is an issue of interpretation. Lee et al. was portrayed as a study finding “genes linked to educational attainment” when this was not the intent
    • A corresponding author Daniel Benjamin stated, “It would be completely misleading to characterize our results as identifying genes for education” (here)
    • the Social Science Genetic Association Consortium (sponsors of Lee et al.) specify in responses to FAQs that they did not find the gene/s for educational attainment (FAQ 3.1, 3.2)(Regardless of which study they refer to on educational attainment, this remains true)
    • The study ITSELF also cautions “Our results also highlight two caveats to the use of the polygenic scores in research. First, our within-family analyses suggest that GWAS estimates may overstate the causal effect sizes […] Without controls for this bias, it is therefore inappropriate to interpret the polygenic score for educational attainment as a measure of genetic endowment”
    • Finally, the study confirms a slew of other studies that there is “lower predictive power in a sample of African-American individuals than in a sample of individuals with an European ancestry” (remember the study only reported an incremental R2 of 1.6% for African-Americans)
  • Addressing PS?
    • Lee et al. specify their work in regards to addressing population stratification (here)
    • However, the study itself (Lee et al. specifies nurturing as a potential area of future research because GWAS results could be overstating causal effects), as well as studies like Kong et al. 2018, specify that “While the correlation between the polygenic score and educational attainment suggests that it can predict around 11–13% of the variation in educational attainment, within-family analyses suggest that at least half of this predictive ability comes from indirect genetic effects from relatives, population stratification, and assortative mating”(quote itself from Young et al. 2019)
    • Richardson & Jones 2019 specify in response to “controls for…population structure” that “the assumption that siblings share the same environment is a misunderstanding of the conditions of human development and social-cognition.” He then cites factors like different treatment that can lead to reactive “within family effects not usually considered in simple additive variance-partitioning models. Any SNPs unrelated to cognitive ability, but adventitiously correlating with those physical attributes, will spuriously correlate with CA/EA.”
    • Even more damning, a 2021 paper by Richardson and others utilizes the method of “negative control” to provide further evidence of the widespread and persistent influence of PS DESPITE attempts at correction. Importantly, this influence seems especially strong for complex traits such as “years of schooling” widely utilized in Lee et al.
  • Other
    • While the impacts of SES differ based on the study (Mostafavi et al. 2020), in samples that are unrepresentative, problems occur. Lee et al. includes many medical data sets and the 23andMe data set, which are not representative of the national population. Furthermore, individuals in the UK Biobank have a higher SES than the rest of the population (Fry et al. 2017). Also plausible is that people who participate in GWAS differ, for different reasons, from those who do not (Taylor et al. 2018). Thus, these results make it more difficult to generalize the results of GWAS-based estimates
    • Mostafavi et al. also compared the “ prediction accuracy when the PGS is trained on ‘unrelated’ individuals such as those used in a standard GWAS to one obtained from a sibling-based (or ‘sib-based’) GWAS (Materials and methods)”
    • They found that there are “lower prediction accuracies for PGS based on sib-based GWAS” which “indicate that complications such as assortative mating or indirect effects contribute to the standard GWAS estimates”
    • The application of Mostafavi’s results to Lee et al. is that they find prediction accuracy can differ even across groups with similar ancestry. They also imply that controlling for environmental variance will not be enough as factors like “differences in the magnitude of genetic effects among groups, indirect effects and assortative mating, also lead to differences in the prediction accuracy of PGS, in ways that may make applications of phenotypic prediction less reliable, even within a single ancestry group”

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Missing Heritability & Nonadditive Variation

Missing Heritability & Nonadditive Variation

• Kim et al. 17
  • The search for missing heritability has NOT concluded
• Mäki-Tanila & Hill 14
  • Overall we conclude that theoretical predictions and experimental observations of low amounts of epistatic variance in outbred populations are concordant. It is not a likely source of missing heritability, for example, or major influence on predictions of rates of evolution.
  • Thus, while it may not contain the missing heritability, it limits individual prediction (here)
• Kaplanis et al. 18
  • Family data: exploited massive volumes of family tree data to estimate additive, dominance and epistatic components to variance, finding small (4%) dominance and negligible (~0%) epistatic contributions
• Zaitlen et al. 13
  • We examine phenotypic correlations across a range of relationships, from siblings to first cousins, and find that the excess phenotypic correlation in these related individuals is predominantly due to shared environment as opposed to dominance or epistasis.
  • For many traits, the explanation that dominance and other nonadditive components can be ‘masked’ by shared environmental variance does not suffice
• de los Campos et al. 19
  • shows that the presence of linkage disequilibrium can generate phantom epistasis and mask it
• Brown et al. 14
  • gene-gene interactions explain ~4.3% of variance, and in some cases interaction explaining more than the additive part
• Dominance variation contributes little to the missing heritability (Nolte et al. 17, Sanjak et al. 16, Zhu et al. 15)

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IQ and g

IQ and g

• Guttman 10
  • Problems with Jensen’s “testing” of Spearman’s Hypothesis
  • the correlations are merely an artifact of the way that the factor/principal component is extracted
  • If this possibility is true, then Nijenhuis & Van den Hoek 2016; te Nijenhuis et. al 2014; te Nijenhuis et. al 2015a; te Nijenhuis et. al 2015b; te Nijenhuis et. al 2016 is useless
• Ashton & Lee 05
  • Problems with Correlated vectors
  • “…several discussions of the limitations of the method of correlated vectors, in the context of investigations of group differences, have been provided by Dolan (2000), Dolan and Lubke (2001), Dolan, Roorda, and Wicherts (2004), Lubke, Dolan, and Kelderman (2001), and Lubke, Dolan, Kelderman, and Mellenbergh (2003). These discussions have focused on two problems: possible violations of the assumption of measurement invariance across groups, and possible group differences in factors other than g.”
• Gignac 15
  • Raven’s Matrices is not a pure measure of general intelligence: Implications for g factor theory and the brief measurement of g
• Richardson 17 (book)
  • Culture loading implicit admission on RPM
  • “For example, tests like the Raven nearly all require the reading of elements in a matrix from top-left to bottom-right, just as in (Western) text. But they also embed further rules than that, far from. Being experience free, mimic those in record sheets, timetables, or other tables of rows and columns, with totals and subtotals. The rules involve additions, subtractions, and substitutions of symbols across columns and down rows, and the deduction of new information from them.”
• Carpenter 90
  • “However, the descriptions of the abilities that Raven intended to measure are primarily characteristics of the problems, not specifications of the requisite cognitive processes. John Raven constructed problems that focused on each of six different problem characteristics, which approximately correspond to the different types of rules that we describe later. He used his intuition and clinical experience to rank order the difficulty of the six problem types.”
  • “In the Raven test, more difficult problems tended to involve more abstract rules than the less difficult problems. (Interestingly, the level of abstraction of even the most difficult rule, distribution of two values, does not seem particularly great compared with the abstractions that are taught and acquired in various academic domains, such as physics or political science.)”
• Hilliard 12
  • A consistent 15-20 point IQ differential existed between the more economically privileged, better educated, urban-based, English-speaking whites and the lower-scoring, rural-based, poor, white Afrikaners. To avoid comparisons that would have led to political tensions between the two white groups, South African IQ testers squelched discussion about genetic differences between the two European ethnicities. They solved the problem by composing a modified version of the IQ test in Afrikaans. In this way, they were able to normalize scores between the two white cultural groups.
• Drame and Ferguson (2017)
  • “Results indicate that use of the Ravens may substantially underestimate the intelligence of children in Mali”
  • European and North American children may spend more time with play tasks such as jigsaw puzzles or connect the dots that have similarities with the Ravens and, thus, train on similar tasks more than do African children. If African children spend less time on similar tasks, they would have fewer opportunities to train for the Ravens (however unintentionally) reflecting in poorer scores. In this sense, verbal ability need not be the only pitfall in selecting culturally sensitive IQ testing approaches. Thus, differences in Ravens scores may be a cultural artifact rather than an indication of true intelligence differences.
• Dutton 17
  • Criticism of RPM in South Sudan
• Mensh & Mensh 1991
  • It is paradoxical to maintain that IQ tests, which are inherently biased, can promote equal opportunity. The tests do what their construction dictates; they correlate a group’s mental worth with its place in the social hierarchy. (Mensh and Mensh, 1991, The IQ Mythology, pg 30)
  • IQ TESTS CANNOT BE FRAMED AS AN EGALITARIAN DEVICE
    • “Like IQ tests, the hereditarian-environmentalist debate is immersed in mythology. In fact, this debate has revolved around IQ testing for so long that the myths surrounding each are not only intertwined but interdependent.
    • According to its image, the nature-nurture debate pits conservatives against liberals. One part of this image reflects reality; part is mythical; environmentalistsm has not only liberal and radical supporters, but many conservative ones as well.
    • One factor that sustains the deabte’s liberal-versus-conservative image is that many environmentalists have condemned the hereditarians’ claims of genetic intelligence differentials between races and classes as a justification for class and racial inequality. At the same time, however, environmetalists present their own thesis — which accepts the claim of class and racial intelligence differentials but attributes the alleged differentials to environment rather than heredity — as an alternative to hereditarianism. But is their thesis in fact an alternative to hereditarianism? Or does it instead — irrespective of the mentions of many environmentalists — result in an alternative justification for class and racial inequality?: (pg. 10-11)
• Hilliard 2012
  • Example of test construction in action
  • In South Africa in the 1950s there was a 15-point difference in IQ between two white cultural groups. Rather than fan flames of political tension between the groups, the test was changed in order to eliminate the difference between the two groups. The same, she notes, was the case regarding IQ differences between men and women—Terman eliminated such differences by picking and choosing certain items that favored one group and balanced them out so that they scored near-equally. These are two great examples from the 20th century that demonstrate the reflexivity in psychology—how one’s a priori biases influence what they study and the types of conclusions they draw from data.
• “Poor people are poor because they have lower IQs”
  • Very simple response (see Richardson 17). Societies are socially stratified. Because of this stratification, a genetic stratification is maintained. Thus, social class stratification creates cognitive differences. IQ tests show these differences (which are as a result of this social class stratification). Also genetic differences between classes are irrelevant to educational attainment (further, see: here, here, here, here, and here)
• Flynn & Nijenhuis 14
  • ‘Responds” (more of an overview) to Nijenhuis’ Is the Flynn effect even on g?
  • Explains paradoxes in the claims made by Rushton and Jensen regarding black IQ
• Reviving Sampling Theories of Intelligence
  • Presents arguments for alternatives to testing intelligence
• Network, Cognitive and Developmental Models of Intelligence
  • Sampling theories+Mutualism
• Richardson (2002)
  • Regarding IQ testing
  • “I shall argue that the basic source of variation in IQ test scores is not entire (or even mainly) cognitive, and what is cognitive is not general or unitary. It arises from a nexus or sociocognitive-affective factors determining individuals: relative preparedness for the demands of the IQ test.”
• Richardson 17
  • GWAS IQ gene associations are correlations which are inevitable and meaningless and due to social class. It’s due to class stratification which cause genetic differences not related to ‘cognitive ability’ or ‘educational attainment’.
  • “In summary, either directly or indirectly, IQ and related tests are calibrated against social class background, and score differences are inevitably consequences of that social stratification to some extent. Through that calibration, they will also correlate with any genetic cline within the social strata. Whether or not, and to what degree, the tests also measure “intelligence” remains debatable because test validity has always been indirect and circular.”
• Piccolo et al. 16
  • SES impact on Brazilian Children
• Jenkins et al. 19
  • SES + Brain volume’s impact on IQ
  • Generalized conclusion: “Parent IQ affected child IQ directly and indirectly through community and educational capital, demonstrating how environmental factors interact with familial factors in neuro-development. There were no intervening effects of cerebral white matter, grey matter, or total brain volume.”
• Gillborn 18
  • Government set standards perpetuate BWIQ gap IRREGARDLESS OF INTENTIONALITY: Analysis of UK + persistence of racialist thought in the US
  • “What matters is the discriminatory impact and potential for encoding racist beliefs into the heart of future policy and practice. The alt-right, like other political lobbyists, think tanks, and pressure groups, do not exist for their own sake—they pursue a long-term goal of changing the nature of political and popular discourse (Stefancic & Delgado, 1996). The superficial absence of race from the language of popular IQ-ism does not denote any lessening in the racist quality of such assertions. Hereditarians frequently set out their plans for highly selective IQbased education, such as Plomin’s “genetically-sensitive school” (Asbury & Plomin, 2013) and Martin’s “smart fraction schools” (2016). Their prescriptions do not mention race, but the racist impact against black students would inevitably follow based on everything we know about the racialized consequences of such profiling, whether by IQ test, teacher assessment, or some combination of the two.” (Gillborn 18 [pg. 74])

• (THOROUGH CHECK REQUIRED) Equal prediction for groups with different means is evidence of bias, not the absence thereof. See Millsap 1997; https://doi.apa.org/doiLanding?doi=10.1037%2F1082-989X.2.3.248

• REBUTTAL TO: Lang et al (2010)
  • Argument: When using a method assuming the “g” exists (which they call “GMA”, general mental ability), then “g” arises as the most important predictor of the job performance (even though the relationship is weak), and specific abilities do not add much explanatory power.
  • Still weak, from Lang et al see: Results revealed that GMA accounted for 10.9% to 28.6% of the total variance explained in job performance and that GMA was not consistently the most important predictor [for jobs with low complexity].
  • Uses supervisor ratings which are subject to bias
• McManus et al. 13
  • In medical school entrants, IQ has no predictive power.
  • “However, individuals within the pool of applicants who fail to get in to medical school because of poor A-level grades never take medical school examinations, so we can never find out whether, had they been allowed in, they would have done as badly as they did in their A-levels, or whether they would have confounded expectations and done well. This restriction of range means that in the selected group (those who enter medical school) the correlation of the selection measure (for example, A-levels) with the outcome measure (for example, first year medical school examinations) will necessarily be weaker than would be the case if performance were to have been assessed across the whole range of medical school applicants. That situation could only be assessed empirically if entrants were to be a random, representative sample from the pool of all applicants, with A-level grades at all levels of achievement. Statistical solutions to the problem of range restriction have been explored for many decades [5] and the problem is now statistically tractable [6, 7], so that validity coefficients for selection as a whole (so-called ‘construct validity’) can therefore be calculated.”
• Richardson & Norgate 15
  • Does IQ Really Predict Job Performance?
  • Hunter & Hunter and other studies that would claim .3-.5 relationship between IQ and job performance are upwards adjustments from .10-.20 true correlations
• Verbeke et al. 08
  • “Consistently, we propose that GMA in itself does not predict job performance. Only in interaction with other aspects of intelligence will it have a significant predictive value in explaining job performance. Such a perspective is called the “factorial view” of intelligence.” [pg. 44]
  • shows that the relationship is contingent upon other characteristics and can reverse in many circumstances
• Byington & Felps 10
  • “This article adds a further empirical dimension to the debate over policy alternatives. Specifically, to the extent that the link between IQ and ultimate job-performance is mediated by developmental resources allocated on the basis of IQ-reflective scores, the case for allocating developmental resources based on IQ is weakened. Said another way, if a society could allocate resources on a given criterion (say, eye-color) and produce a strong correlation with job performance, then differentiated resource allocations become less justifiable. Conversely, the IQ-based allocation of developmental resources becomes more justifiable to the extent that: (1) variance in ultimate job performance is explained by immutable differences in mental processing and (2) these differences are captured by IQ tests. In short, there are a number of empirical questions that need to be answered before it is possible to make moral or policy judgments. Foremost: to what extent is the connection between IQ and job performance a function of institutionalized, IQ-based allocations of developmental resources?”
  • After exploring what was missed by the studies such as Hunter & Hunter, Schmidt & Hunter, etc…. whatever correlations remain can be explained as the result of socially contingent institutional practices
• Yong 17
  • Yong argues that Nobel Prizes are an “absurd and anachronistic way of recognizing scientists for their work. Instead of honoring science, they distort its nature, rewrite its history, and overlook many of its important contributors.”
  • Should not be used to judge the intelligence or development of a society

Brain size correlations

• Pietschnig et al. 15 (look back over)
  • META-ANALYSIS: pre-registered studies show lower correlations between .12 and .24 (along with study below)
  • “We show that the strength of the positive association of brain volume and IQ has been overestimated in the literature, but remains robust even when accounting for different types of dissemination bias, although reported effects have been declining over time. While it is tempting to interpret this association in the context of human cognitive evolution and species differences in brain size and cognitive ability, we show that it is not warranted to interpret brain size as an isomorphic proxy of human intelligence differences.”
• Schonemann et al (2000)
  • Show that brain size does not predict general cognitive ability within families
  • Used to respond to Rushton & Ankney 09 although not directly

Fade-out Effect

• Stankov & Lee 2020
  • We Can Boost IQ: Revisiting Kvashchev’s Experiment
  • “cognitive abilities captured by the tests of intelligence may not be fixed entities, since prolonged and intensive training in creative problem-solving within typical school environments can lead to sizable and positive gains in the overall cognitive function in late adolescence (ages 18–19).”
  • Finds Kvashchev’s rebuttal of Jensen 1969 (through a 1980 Yugoslavian Intervention study finding IQ gains) too conservative (find large gains on both Gc and Gf factors)
  • Note that Simons et al. 2016 and its critiques do not apply to the extremely rigorous methods of Kvashchev’s experiment (28 tests of intelligence, large sample size, etc…)
  • Jensen’s claims about the failure of education in boosting “intelligence” has already been refuted by the likes of Hegelund et al. 2020
  • Even after the training ended, performance was still increased in the experimental group
• Sauce & Matzel 18
  • “Absent the opportunity to assimilate into an environment that is matched to their new cognitive capacity (a forced loss in gene-environment correlation), it would be difficult to maintain or amplify the initial benefits afforded by the early intervention. Thus, much like the inter-generational Flynn effect, increases in IQ might be amplified, or at least sustained, by greater access to opportunities that often are inequitably distributed. In simpler terms, the analysis of Protzko should not lead us to conclude that early intervention programs such as Head Start can have no long-term benefits. Rather, these results highlight the need to provide participants with continuing opportunities that would allow them to capitalize on what might otherwise be transient gains in cognitive abilities.” (Sauce & Matzel 18)
• Howe 1997 (Only part of the book)
  • Howe examines Zigler & Seitz’s study. They measured the effects of a four year intervention program which emphasized math skills. They were inner-city children who were enrolled in the program at kindergarten. The program was successful, in that those who participated in the program were two years ahead of a control group, but a few heads after in a follow-up, they were only a year ahead.
  • Howe explains why: “For instance, to score well at the achievement tests used with older children it is essential to have some knowledge of algebra and geometry, but Seitz found that while the majority of middle-class children were being taught these subjects, the disadvantaged pupils were not getting the necessary teaching. For that reason they could hardly be expected to do well. As Seitz perceived, the true picture was not one of fading ability but of diminishing use of it.” (How 1997 pg. 54-55 [included in cut sections of the book unfortunately; non-paywall access not available])
• Bauer and Schanzenbach, 2016
  • Effects of Head start Program
  • Adults who were in the HS program are more likely to graduate high school, go to college and receive a secondary degree
  • Reject claims that Fade-out Effect nullifies these programs
• Barr & Gibbs 17
  • Breaking the Cycle? Intergenerational Effects of an Anti-Poverty Program in Early Childhood
  • “Leveraging sibling comparisons and discontinuities in grant-writing assistance and program eligibility, studies have documented increased educational attainment, better health, higher earnings, and less involvement in risky behaviors (Carneiro and Ginja 2014, Deming 2009, Garces et al. 2002, Ludwig and Miller 2007), even in the presence of short-term test-score fadeout (Deming 2009). A recent follow-up to Deming’s study looks at even longer-term outcomes and finds persistence of effects later into adulthood, including impacts on participants’ later-life parenting practices (Bauer and Schanzenbach 2016).”

Mutualism

• Why Your Mind Is Like a Shark: Testing the Idea of Mutualism
  • Extremely basic overview of Mutualism
• Van der Maas et al. 06
  • Original model
• Van der Maas et al. 17
  • General overview of theories
  • Support for Network models
  • Formal revised model implementing things such as the multiplier of Dickens & Flynn
    • “These multiplier effects can also occur across domains. In this way, the mutualism/Dickens model incorporates gene by k interactions and possibly explains the so-called Flynn and Jensen effects”
• Conway & Kovacs 15
  • Psychometric g is still put in question

Formal Evidence

• Kievit 20
  • Evidence for mutualism is strong
  • Used to test/observe phenomena: “This theory [Mutualism] is fully formalized [2,3] - In other words, it can be expressed as a series of mathematical equations that capture explanatory principles, and allow for the reproduction of key observed phenomena.”
• Schmank et al. 19
  • Further support for network models
  • found support for mutualism in the WAIS-IV cognitive battery (Wechsler, 2008), such that a network model showed better fit to the pattern of intelligence scores compared to a latent variable approach (g-factor).
• Hofman et al. 18
  • The dynamics of the development of mathematics skills: A comparison of theories of developing intelligence.
  • EVIDENCE OUTSIDE LABORATORY ENVIRONMENT
• Kan et al. 19
  • Extending psychometric network analysis: Empirical evidence against g in favor of mutualism?
• Kan et al. 20
  • Clarifies approach in Kan et al. 19
  • To choose the best model is a matter of BOTH theory and fit (thus studies like Panizzon et al. 2014 are discarded in comparisons)
  • Responds to Penta-factor model by Mcfarland
  • “The comparison between the second-order g factor and the psychometric network models was considered to deliver an answer to the following question. Which among these two models provides the best theoretical explanation of the variance–covariance structure among the observed variables? Is it g theory (Jensen 1998) or a network approach towards intelligence (consistent, for example, with the mutualism theory (Van Der Maas et al. 2006))?”
  • “First, we pointed out that the penta-factor model is nonidentified and is therefore of only limited value as a model of general intelligence. Second, we proposed an alternative (and correct) method for comparing psychometric networks with latent variable models. If researchers want to compare network and latent variable models, we advance the use of the R package Psychometrics”
  • “The AIC and BIC values also suggested that among all models considered, the network model provided the best statistical summary, as it also outperformed the measurement model and the bifactor model”
• Schmank et al. 2021
  • Checking and reconfirming Schmank et al. 2019
  • Refuting McFarland’s contentions regarding latent vs psychometric network modeling
• Kievit et al. 17
  • Mutualistic Coupling Between Vocabulary and Reasoning Supports Cognitive Development During Late Adolescence and Early Adulthood
  • “Empirical evidence in favour of the mutualism model has been emerging in the literature. For instance, in 784 adolescents (age 1424) Kievit et al. [6] found positive, reciprocal coupling between vocabulary and reasoning over time – In other words, children with greater vocabulary skills showed more rapid gains in fluid reasoning and vice versa.” (from Kievit 20 above)
  • In both these papers (Kievit et al. 17 & 19) a quantitative representation of a developmental general intelligence factor more poorly fit the data.
• Kievit et al. 19
  • Replication & extension of Kievit et al. 17
  • Mutualistic Coupling Between Vocabulary and Reasoning in Young Children: A Replication and Extension of the Study by Kievit et al. (2017)
  • In both these papers (Kievit et al. 17 & 19) a quantitative representation of a developmental general intelligence factor more poorly fit the data.
• Ou et al. 19
  • Modeling person-specific development of math skills in continuous time: New evidence for mutualism: used complementary analytic approaches in an educational game setting to tease apart coupling effects among distinct mathematical skills.
  • used a continuous time random effects model to demonstrate that counting and addition provide mutually beneficial interactions over time
  • EVIDENCE OUTSIDE LABORATORY ENVIRONMENT
• Peng & Kievit 19
  • The Development of Academic Achievement and Cognitive Abilities: A Bidirectional Perspective
  • “Increasing empirical evidence supports the assumption of mutual influences between cognitive processes, including in a recent meta-analysis that found fluid intelligence and reading/mathematics to predict each other over time” (characterization by Uka et al. 19)
• Peng et al. 20
  • META-ANALYSIS
  • Observed strong, reciprocal effect between language and mathematics, showing robust (partial r = .20/.22) effects such that higher language at baseline was associated with more rapid mathematics gains, and vice versa.
  • Though, note the specific limitations for argumentative use
• Peng et al. 19
  • META-ANALYSIS
  • Further support for mutualism (Peng’s meta-analyses prove problematic for critics of the model who purport to find a lack of research demonstrating increasing correlations with age as “Gf and reading/mathematics predict each other in the development and their relations increase with age, suggesting a reciprocity between Gf and reading/mathematics.”
  • Findings: (a) Gf (fluid intelligence) showed stronger relations to mathematics than to reading, (b) within reading or mathematics, Gf showed stronger relations to complex skills than to foundational skills, (c) the relations between Gf and reading/mathematics increased with age, and (d) family social economic status (SES) mostly affected the relations between Gf and reading/mathematics in the early development stage.
  • Age differentiation vs Age (de)differentiation is STILL inconclusive (in line with Van der Maas’s statements on this topic. The study’s results support (de)differentiation
    • Age De-differentiation: Baltes et al. 1999, Hülür et al. 2015, Kievit et al. 2017, etc…)
    • Age Differentiation: Gignac 2014, Hülür et al. 2011, Tucker-Drob 2009, Tucker-Drob & Salthouse 2008, etc…
    • Maier et al. 2021 has already tested Mutualism vs g accounting for differentiation effects and the mutualism model is still superior
    • For a very recent review, see Breit et al. 2020
    • Find support and opposition to ability differentiation, developmental differentiation inconclusive due to methodological errors such as utilizing grouping (which has been widely criticized)(These errors are seen in: Jensen 2003, te Nijenhuis & Hartmann 2006, Arden & Plomin 2007, Bloom et al. 1988, Osborne & Suddick 1972)
    • Even with model-based methods as seen in Makris et al. 2017 and Reynolds 2013 are not consistent across age levels and cognitive domains
    • The present study’s results on developmental differentiation:found effects not statistically different from zero indicating that both Cattell and van der Maas’s theories are not aligned with the results (inconclusive)
    • The present study’s results on ability differentiation found evidence only on verbal ability. The results are in line with the previous piece of evidence above (Maier) that the positive manifold can arise without consistent ability differentiation effects (in line with the Mutualism model)
• Marevana & Holmes 20
• Network models were used to compare the associations between cognitive and academic skills in struggling learners and children drawn from the general school population;
• Despite substantial differences in cognitive and academic abilities, many similarities were observed in task connections together with a few notable differences;
• Academic abilities were more closely related in the typical learners, while maths skills were more strongly linked to domain-general executive abilities in the struggling learners;

• Psychometric network models are a useful tool for exploring and comparing the links between cognition and learning across populations.

• Palmer et al. 20
  • Further evidence in favor of mutualism and against g
• McFarland 20
  • Mutualism is very different from g (one early criticism of mutualism was of the difference). The authors stress how the validity of the mutualism model, if true, would render the search for a gene or brain area “for g” useless (also see Van der Maas et al. 2014). Also, smaller correlations regarding mutualistic relationships are irrelevant as positive manifold still arises (pg. 113)

More Mutualistic Effects

• Sewasew & Schroeders 19
  • Found that standardized tests of verbal and mathematical ability showed strong, positively reciprocal effects between the two domains across 3 waves (grade 1, 3 and 5).
• Quinn et al. 15
  • observed that higher vocabulary scores were associated with more rapid gains in reading ability – But not vice versa.
• Lervåg et al. 18
  • used a correlated latent growth curve model to show strong positive associations between baseline reading comprehension and growth in vocabulary, and vice versa, and a positive effect between baseline fluid reasoning and rate of vocabulary increase (but not reading ability).
• Elliot et al. 18
  • Investigated the potential bidirectional associations between approximate number sense and mathematical ability. In line with mutualism, early approximate number sense was associated with greater growth in mathematical ability, as well as vice versa.
• Prat et al. 20
  • Demonstrated that the rate of learning of coding skills in an intervention was positively associated with a battery of traditional cognitive tasks, especially so for language and fluid reasoning skills. Most strikingly, in a simultaneous regression model, language aptitude, fluid intelligence and working memory updating made unique, and similarly strong, positive contributions to learning rate, suggesting multiple distinct cognitive drivers of the rate of coding acquisition.

Rebuttals

• Gignac (2014) and Gignac (2016a) profess to present empirical evidence against mutualistic models, but see van der Maas & Kan (2016). Gignac (2016b) responds by appealing to parsimony, but see here Roche 18

• Jensen effect (as well as Woodley of Menie & Meisenberg, 2013; Rushton & Jensen, 2010; Woodley of Menie, 2011): criticism of mutualism regarding Jensen effect (correlation between the vector g loadings and the heritability coefficients of tests using MCV) is explained away by van der Maas 2017 (also seen in McFarland 20 pg. 116)

• Criticism from Nisbett et al. 12 is partially incorrect (see the next page)

• Regarding Shahabi et al. 18 that replicated Gignac 14’s claim that g factor was stable from 3 years on (seen as an indirect argument against mutualism):
  • 1. The Mutualism model doesn’t specify the age at which statistical g factor should become stable (McFarland 20 pg. 117)
  • 1. it is unclear what g predicts but see Gignac’s argument:
       • “According to Gignac, g factor theory may be suggested to predict the strength of the g factor to be largely constant across all ages, because this theory postulates biological and genetic substrates for g” (pg. 117) (claim originally from pg. 90 of Gignac 14)
  • 1. Data on age differentiation and (de)differentiation is rather inconclusive (ex. Hofman and Gignac papers clash)
  • Regarding Gignac’s argument, “But the biological substrate is all but constant in childhood, and g-theory unspecific with regards to such developmental issues.”
• Appeals to parsimony (Gignac 2016b already addressed above)
  • The g model is more expensive despite the amount of parameters in the mutualism model
  • Authors do not assume some mysterious latent variable that itself comes with assumptions (Conway et al. 2021, Goring et al. 2019)
  • Finally, Gignac’s paper is dated. We already know Mutualism is better, in theory and fit, than g
• Far transfer
  • “Moreover, M can be sparse and still give rise to a positive manifold. Many kinds of restrictions may be placed on the elements in the matrix M, which can be based on theoretical considerations, or the results of experimental studies. To explain the positive manifold, it suffices to assume Mij = c for all subjects.” (Van der Maas et al. 2019)
• Finally, the recent successes in finding genome sequence differences accounting for significant contributions to the heritability of intelligence isn’t an argument against mutualism. They don’t prove g is a biological causal entity. Studies cited to this effect like Plomin & Stumm 2018 are actually inconsistent with the biological unitary g factor as they find genetic effects on intelligence pleiotropic and 100,000s of SNP associations are needed to account for the 50% heritability estimate estimated by twin studies.

Possible Policy

• Simpson-Kent 20
  • Mutualism based classroom policy?

Collective Intelligence

• Woolley et al., 2010
  • First study showing a CI-factor
  • Collective Intelligence is a far better predictor of group performance on the tasks tested than either average IQ of the members or the highest member IQ
  • Furthermore, average IQ correlated only 0.15 of CI, while maximum member IQ correlated 0.19, not very high.
• Rowe et al., 2021
  • Results indicated a moderate correlation, r , of .26 (95% CI .10, .40), between the CI-factor and group performance.
  • A meta-analysis of the subset of studies which averaged out the IQ of group members found the average IQ of the groups’ members had little to no correlation with group performance ( r = .06, 95% CI −.08, .20).
• Riedl et al., 2021
  • Meta-analysis of 22 studies which finds an average correlation between CI and group performance of r = 0.4, with subtests ranging from 0.27 to 0.52.
  • Further finds that individual skill in each subtest can only explain ~30% of the CI on average
• Graf-Drasch et al., 2021
  • Solves a problem of replicability; it turns out that while CI remains one factor in well-structured as well as creative tasks, it turns into several factors for ill-structured tasks.
  • Well-structured tasks had high correlations with each other, while the same was true for brainstorming, the creative task.
  • The average correlation of well-structured tasks is r = 0.49, while the correlation for creativity on the first factor is r = 0.35.

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Race and Craniology

Race and Craniology

There is a huge section about this in this document debunking scientific racist myths, which debunks both the premises behind studies of craniology in race, and debunks many specific studies used by racists

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Public Perception (Regarding Genetics)

Public Perception (Regarding Genetics)

` `“… people seem to deploy elements of fatalism or determinism into their worldviews or life goals when they suit particular ends, either in ways that are thought to ‘explain’ why other groups are the way they are or in ways that lessen their own sense of personal responsibility (Condit, 2011)” (Alexander, 2017: 17-18).

the public understands genes as playing more of a role when it comes to bodily traits and environment plays more of a role when it comes to things that humans have agency over—for things relating to the mind (Condit & Shen 11)

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Testosterone Differences?

Testosterone Differences?

Often cited by hereditarians are inaccurate claims on the differences of Testosterone (T) between races and its implications. Here are some sources often cited by hereditarians to support their hierarchical view on testosterone differences and counterarguments.

• Lynn 2013
  • Claims “Testosterone is a determinant of aggression” from the sources (Book, Starzyk, & Quinsey, 2001; Brooks & Reddon, 1996; Dabbs, 2000) (pg. 2)
    • This is inaccurate as there is only a correlation between aggression and testosterone (Book, Starzyk and Quinsey, 2001 shows a .14 correlation, Archer, Graham-Kevan, and Davies 2005** show a lesser .08 correlation).
    • **Problems arise as, from Sapolsky 1997 see: Okay, suppose you note a correlation between levels of aggression and levels of testosterone among these normal males. This could be because (a) testosterone elevates aggression; (b) aggression elevates testosterone secretion; (c) neither causes the other. There’s a huge bias to assume option a while b is the answer. Study after study has shown that when you examine testosterone when males are first placed together in the social group, testosterone levels predict nothing about who is going to be aggressive. The subsequent behavioral differences drive the hormonal changes, not the other way around. (pg. 113)
    • Further, Brooks & Reddon also show a relationship and nothing more. Dabbs 2000 relates to prisoners and found that violent prisoners had higher testosterone. However, they ignore that being aggressive stimulates testosterone,not that testosterone causes aggression
• Edward Dutton’s 2015 Presentation
  • Edward Dutton claims: blacks are expected to be the highest on testosterone, East Asians are the lowest, and Europeans are intermediate.
    • This is directly refuted by McGreal**
• Ross et al. 1986
  • One of the most cited pieces of literature to make the claim that black people have higher levels of testosterone than whites
  • The study tests the hypothesis that black males were exposed to more testosterone in the womb and this then drove their higher rates of prostate cancer later in life. They come to the conclusion that the mean total testosterone level for black people was 19 percent higher than white people whereas free testosterone was 21 percent higher. The adjusted level ended up being 15% however, something Rushton 1997 ignores.
    • Testosterone does not cause prostate cancer, for one (see Stattin et al. 2003, Michaud, Billups, and Partin, 2015, Boyle et al. 16 )
    • Further, their assay times between 10 AM and 2 PM present an issue of differences in testosterone as the times change. This is still a major confound. Even taking into account their claims of adjusting for this variable (pg. 47). So is waist circumference which wasn’t controlled for
    • Their sample size is also small (50 blacks and 50 whites) and done on college students meaning it isn’t representative on a larger scale
    • Finally, more recent analyses like that of Richard et al. 14 find a smaller 2.5 to 4.9 percent difference
    • What can explain higher levels of prostate cancer then? One answer often posited by people like RR could be that of nutrition (see: here, here, and here)
• Ellis & Nyborg 1992
  • Found that black people had 3 percent higher levels of testosterone than white people.
  • Refuted here: Fish 2013

• Furthermore, claims on the supposed higher level of testosterone of Black women compared to White women are inaccurate: see here

• What about Asians? See here

• Now, because the racial differences in testosterone have shown to be vastly lower than hereditarians claim, connections to crime are also inaccurate. no matter if it’s prenatal androgen exposure or not, the evidence is clear that testosterone is not the cause of things like prostate cancer, aggression, and crime (for crime see here and here)

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Race & Crime

Race & Crime

• Hales et al. 2009
  • Finds that an individual’s ethnic group was not significantly associated with increased or reduced likelihood of offending.
• Harrell et al. 14
  • Report from the Bureau of Justice Statistics
  • ‘Persons in poor households had a higher rate of violence involving a firearm (3.5 per 1,000) compared to persons above the FPL (0.8-2.5 per 1,000)’
  • After accounting for poverty and urbanity (which are strong predictors in crime levels), ‘Poor urban blacks (51.3 per 1,000) had rates of violence similar to poor urban whites (56.4 per 1,000)’
• Ennett et al. 98
  • Findings suggest that racial differences in violent behavior only exist among young adults of low socio-economic status (SES).
  • Exposure to violence, a correlate of SES, accounted for racial differences in violent behavior within the low SES subgroup.

• https://sci-hub.se/10.1086/597285

• https://www.pnas.org/doi/10.1073/pnas.1722210115 https://sci-hub.se/https://doi.org/10.2307/2580353 https://sci-hub.se/https://doi.org/10.1080/01639620802168908

1. “Middle class black people commit more crime than poor white people.”

Actually, no, that’s not at all true. In fact, while there is a correlation between black people and Hispanics and crime, the data imply a much stronger tie between poverty and crime than crime and any racial group, when gender is taken into consideration. The direct correlation between crime and class, when factoring for race alone, is relatively weak. When gender, and familial history are factored, class correlates more strongly with crime than race or ethnicity. Studies indicate that areas with low socioeconomic status may have the greatest correlation of crime with young and adult males, regardless of racial composition, though its effect on females is negligible. A 1996 study looking at data from Columbus, Ohio found that differences in disadvantage in city neighborhoods explained the vast majority of the difference in crime rates between blacks and whites, and a 2003 study looking at violent offending among juveniles reached similar conclusions. http://critcrim.org/barak.htm https://onlinelibrary.wiley.com/doi/10.1111/j.1745-9125.2000.tb00900.x https://doi.org/10.1093%2Fsf%2F75.2.619 https://doi.org/10.1111%2Fj.1745-9125.2003.tb01002.x https://doi.org/10.1080%2F07418820300095441

2. “If poverty causes crime, why aren’t the black and hispanic crime rates the same, since they have similar poverty rates?”

Because that’s not how “causation” works. There’s no one factor that just directly drives something like “homicide rates”. Community violence is a confluence of multiple factors: “Neighborhoods’ incidence of violent crime is related to an array of intertwined characteristics, including poverty, segregation, and inequality; collective efficacy, disorder, trust, and institutions; job access; immigration; residential instability, foreclosures, vacancy rates, and evictions; land use and the built environment; neighborhood change; and location of housing assistance. These characteristics can be both the cause and result of violent crime.” Every case of violence is going to have a literal universe of causative factors behind it unique to each person, place, and thing involved. To follow up, while poverty is generally accepted as having a positive correlation with homicide and likely playing a causative role it may not affect each population at the same rate and other factors are also driving that rate up or down. Additionally rather than looking at poverty alone it’s more common to look at broader structural differences. These can include poverty, unemployment, education levels, unemployment, racial segregation, and urban environments, among other factors. Again these factors may not have the same impact in each population. For example the above study found levels of college education in black populations had negative effects on homicide rates approximately two or three times larger than those found in white or latino populations. Larger percentages of people born outside of the United States had a positive impact on homicide in white populations but a negative impact on latino populations. Another idea that may help explain the higher homicide rates in black populations are the southern culture of violence. The idea is that higher rates if violence in the south east can be partially explained by cultural ideals emphasizing personal honor and condoning lethal violence. One study found that higher homicide rates were correlated with higher percentages of southern born whites in the area both in and outside of the south. Since the south has much higher black populations this could disproportionately impact overall black homicide rates. Similarly there’s been an increasing importance placed in transgenerational trauma. While there haven’t been as many studies on African American populations those performed on families of holocaust survivors suggests that the impacts of trauma on previous generations can directly impact the chances of psychological disorders in their descendants. This is likely due to changes in parenting styles and worldviews. I know there is some ongoing research into epigenetics and generational trauma but I don’t feel comfortable extrapolating conclusions from it. So the basic idea is that the lasting psychological impacts of slavery and violent racism continue in to the present generation. To expand, poverty itself does not “cause” homicide as some malevolent force. It is best to understand poverty in terms of protective and risk factors. For example, if you are poor, you may lack education, which may also mean you lack employment, which means you may be on the streets more often, have more opportunities to enter a fight or assault someone, and perhaps also more reasons to do so (less to lose, more needs, …). It also means you live in worse neighborhoods, in worse material and social conditions. However, there is more than just “poverty” to what characterizes a neighborhood! In terms of protective factors, see for example De Fronzo’s 1997 study about welfare and its impact on homicide rates (which concludes that reducing welfare may increase homicide rates). Thus, Tittle and Meier wrote in 1990: “Where does this leave us, then, in trying to account for delinquency with the help of SES? It appears, on the basis of the recent evidence, that SES may not be nearly so important as many seem to think (Braithwaite, 1981; Kleck, 1982; Nettler, 1978, 1985), but it may well be more important than others have concluded (Tittle et al., 1978). But the circumstances under which individual SES plays a role in delinquency production remains elusive. Sometimes SES does appear to predict delinquency; most of the time it does not […] SES may be a poor proxy for the numerous causal variables that are supposedly embodied within it.” Wright et al. wrote in 2006: “SES has a negative effect upon delinquency through some mediators, that SES has a positive effect upon delinquency through other mediators, and that these negative and positive effects coexist and can cancel each other out. As a result, there can be many causal links between SES and delinquency but little overall correlation.” And according to Brookman and Robinson in 2012: “As Levi (1997: 860) noted, macrolevel accounts ‘seldom generate anything close to a causal account which makes sense of nonviolence as well as of violence’. Put another way, the vast majority of individuals who live in conditions of poverty or disadvantage do not resort to violence at any time. Hence, in order to understand the patterns of violence that actually occur, it is imperative to study the social experiences of those who engage in it (Athens 1992).” Putting aside poverty, there are several other concepts to keep into account, such as inequality and relative deprivation, which are not synonyms of poverty or absolute deprivation. Note that not all of these disadvantages are equally distributed among minority groups, such that specific disadvantages can afflict African Americans to a greater extent than Hispanic Americans.

3. “Being poor doesn’t excuse crime.”

No one is claiming that it does, but if you are poor, you are statistically more likely to commit a crime. It isn’t a determinant of whether or not you will, and statistics only describe qualities of a sample or population, not individuals.

4. “black-on-white rape is far more prevalent than white-on-black rape, according to 2008 BJS numbers.”

• Those numbers use absurdly small sample sizes, something which the BJS doesn’t do anymore
  • “In other words, a sample size this tiny simply cannot be understood to tell us anything about what’s going on at the population level. The odds of picking any 10 or fewer women out of a crowd whose experiences have nothing to do with what’s typical are far, far too high.”
  • Lynn Langton, the person who looked over those sorts of stats at BJS, said herself that they don’t stand by that data anymore due to its lack of reliability
• A 2017 report from the BJS, using more reliable data, actually disproves the claim and instead suggests that even when you take rape and group it up with other similar crimes, you see that black-on-white crime (14.7% of crime on whites) is roughly the same as white-on-black crime (10.9% of crime on blacks). Keep in mind that this is before controlling for factors like socioeconomic status, family relations (the vast majority of rape victims know their perpetrator personally), etc.
  • The obvious conclusion of the numbers in the report is that intra-racial rape and related crimes are much more common than inter-racial crime, not that black-on-white crime is much worse than white-on-black crime. This makes sense too once you consider that most criminals don’t care what your skin color is and most rob in their own neighborhood or close by, and what kind of people are you going to find in predominantly black neighborhoods? Black people.
• Shihadeh et al 96
  • Black isolation emerges as a strong predictor of the rates of black violence in major U.S. cities, a finding that may account for prior evidence of a link between segregation and violence at the macro level

5. “Asian people were oppressed and interned during ww2 and discriminated against afterwards, so why aren’t they as bad off as blacks are?”

• These days, the majority of America’s Asian population is only one or two generations removed from legal immigrants who came to America for merit-based citizenship. That automatically put them, on average, at an advantage – even over poor white Americans.

• Immigration from Asia was historically suppressed by legislation like the Chinese Exclusion Act (1882) and the Immigration Act of 1924. It wasn’t until the Immigration and Nationality Act in 1965 that immigration from Asia boomed. In just over 50 years, the population of Asian Americans went from 980,000 in 1960 to 20.4 million in 2015 – 1960 was after the WWII concentration camps. Today 72% of the adult US Asian population was born outside of the US.

• Being a heavily-immigrant population, Asian Americans on average have a better education and background compared both to the average white or native-born American and compared to the general populations in their country of origin, due to selection processes by America’s immigration system.
  • Poor Asian and Indian communities have much lower crime rates.
• This is often because they have vastly lower population densities. But Vietnam and Cambodia have much higher crime indices than the United States and multiple African countries, and Bangladesh has a higher crime index than most African countries.

6. “But look at Chicago!”

• ⁠We know Chicago has “more crime”, there are more people there. But when you actually compare the amount of crime to the population, Chicago doesn’t even break the top 25 worst cities (18.26 murders per 100,000).

• https://www.reddit.com/r/dataisbeautiful/comments/wa8xhk/top_4_most_common_crimes_in_chicago_from_2001_to/?utm_source=share&utm_medium=ios_app&utm_name=iossmf

7. “Black-on-black crime is high

• Most crime is within races anyway, applies to whites too

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Taboo on Race & Intelligence is Nonexistent

Taboo on Race & Intelligence is Nonexistent

Jackson & Winston 20

• Analysis of 4 core arguments hereditarians make in their claims about being silenced
  1. Experts in the topic broadly agree that there is good scientific support for the idea that there is an important genetic contribution to enduring differences in intelligence and other socially important traits between the biologically real White and Black races.
  2. The ideas expressed in #1 are strictly scientific and must be evaluated as nonpolitical ideas. (Note: race, and IQ research is inseparable from social policy.)
  3. This scientific fact is opposed for political, not scientific reasons. Those reasons include a blind adherence to unscientific doctrines of human equality and liberal bias
  4. The opposition to hereditarian scientists takes the form of character assassination, unfair prohibitions on publishing, and funding of their research, employment insecurity, physical threats, and physical attacks.
• All four are misconstrued or false in some way or another
• Claim 1: “Hereditarians are thus left with the improbable position that their work is stifled and difficult to publish while simultaneously claiming they have established firm conclusions and achieved wide acceptance.”
  • Cofnas 20, Meisenburg 19, and others would claim hereditarian research is being stifled or is more difficult to publish while also claiming “that their position on race differences in intelligence is strongly and widely supported by others in the field.”
  • They would cite Snyderman 88 or Rindermann’s surveys to prove that their claims are accepted by the academic community but cite anecdotal evidence for the difficulty of publishing their work due to “Equalitarian Dogma” (as seen in Clark & Winegard 20)
  • Even so, these surveys are abundant with issues including taking opinions of researchers from different fields, only hereditarian researchers at times, and low response rates
  • If anything, there is a publication leniency, (as seen in Panofsky 14) that hereditarian publishing is tolerated as controversy generates publicity
• Claim 2: “Claims about media coverage require empirical evidence but hereditarians seldom provide any (e.g., Jensen quoted in Miele, 2002, p. 79; Woodley of Menie et al., 2018). In the well-established field of science communication, judging the accuracy of media coverage of science requires longitudinal and comparative studies based on a wide range of resources” (Random quotes from Rushton & Jensen about the media with no backing is not sufficient)
• Claim 3: While researchers in the past may have faced more repetitive and common harassment, this does not occur after publishing of The Bell Curve by Murray & Hernstein
  • Jensen himself said before his death that besides one 1999 incident, harassment against him had stopped
  • Carl and Woodley of Menie (2019) attempt to document “controversies” in intelligence research but things to note:
    • “First, they do not report on the field of intelligence research, but only the much smaller field of research that focuses on race or gender differences in intelligence”
    • Second, they “decided not to exclude incidents just because the person concerned was not an intelligence researcher per se” (Carl & Woodley of Menie, 2019, p. 1), thus, they include people who have never researched intelligence.
    • Third, they only include people who advocate the hereditarian position on race/gender and IQ; anyone opposing that position is not listed even if they were involved in a controversy; thus Jensen is listed as being involved in “controversies,” but none of his intellectual opponents are listed, although logically they must have been as involved as Jensen was.
  • Of the incidents reported by Carl and Woodley of Menie (2019), there are six physical “threats” listed since 1995, some of which are poorly documented (such as against that of Nijenhuis) and include protests against Charles Murray who is not a hereditarian researcher but a conservative writer using hereditarianism to justify his own racist beliefs (even then, the protests at universities, for example, resulted in the students being reprimanded and Murray being invited again)
  • “The point is not to engage in some sort of scorekeeping about which side of the issue has suffered the most but rather to point out that hereditarians who may face harassment do not do so because only one side of racial issues is “allowed” to be discussed publicly.” (if this claim by hereditarians were the case then we would not see a rise in right wing violence against antiracist and feminist teachers (Lieberman, 2017)
• Claim 4a: Unfair Denouncements
  • From Panofsky 14:
  • “swaggering, aggressive disposition was more than a way to weather protests. It animated an approach to building the symbolic and material resources for securing scientific credibility and recognition, or scientific capital. For these behavior geneticists, the task was . . . to engage in polemical scientific attack, declaring themselves as crusaders who would rout the antigenetics heresy gripping behavioral science (p. 141). Pearson’s character assassinations suited this crusade perfectly.”
  • In sum, while claiming to be victims of “unfair denouncements”, hereditarians such as Pearson and Sesardic often call those who disagree with their positions “Leftists” and “Marxists” (Pearson 1991; Sesardic 05 ; Taberny 15 (in the sesardic section of the SL)
  • Also: “Hereditarians cannot reasonably claim that there is a taboo on objective, value-free scientific research and simultaneously make alliances with, cite as authorities, and publish under the auspices of the most extreme-right racist figures on the political landscape. Nor can they justify attacking their critics as issuing fallacious ad hominem arguments by pointing out these connections.” (as we see in Carl & Woodley 19 where they defend Nijenhuis against attacks of his involvement in “neo-fascist” organizations)
• Claim 4b: Loss of Jobs
  • Once again, Carl & Woodley cite:
    • “8 individuals lost full-time jobs or temporary positions . . . : Noah Carl, Frank Ellis, Gerhard Meisenberg, Bryan Pesta, Jason Richwine, Alessandro Sturmia, Larry Summers, and James Watson. In addition, three other individuals lost work at least in part because of a “communication” related to psychometric intelligence. (Christopher Brand, Toby Young and Thilo Sarrazin; p. 2)” (from Carl & Woodley 19)
  • They misconstrued the context of each firing and even cite nonscientists (see full study)
• Finally, while there certainly is no movement to silence hereditarians through the means listed above, :
  • “…there is a substantial scholarly literature that is almost never discussed or cited. We refer to the five decades of careful, archival investigations documenting the involvement of psychologists and the Pioneer Fund with the campaign to overturn the Brown decision and preserve segregation, anti-immigration activism, and active involvement with neo-Nazi groups (Billig, 1979; J. P. Jackson, 2005; Lombardo, 2002, 2003; Newby, 1969; Saini, 2019; Tucker, 1994, 2002, 2003, 2009; Winston, 1998)”

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Modern Eugenics

Modern Eugenics?

• Wilson 2019
  • Attempts to redefine eugenics represents a blatant disregard for history and policy implications
  • Even then, modern attempts at redefining eugenics to fit the hereditarian narrative are confusing
  • The aftermath of WWII did not cause a slew of anti-hereditarian beliefs to suddenly arise (arguments against race, arguments against g, etc….)

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Certain Authors

Certain Authors

John Philippe Rushton

• Former head of Pioneer Fund: designated as hate group by SPLC
• Cherrypicks data: In one instance, he cites magazine forums and a semi-pornographic book to make claims about racial differences in sexual characteristics(McGreal 12) -engaged in borderline illegal practices to gain evidence ( paid 50 whites, blacks, and Asians to answer questions about their sexual habits. Rushton didn’t inform the students that the survey hadn’t been approved by the university, and many students likely felt pressured to participate so as to not offend their professor. His Administration reprimanded him. (Edwards 19)
• Destroyed on any theory or claim he made; response by Rushton against these rebuttals was to claim that he was targeting for ideology (his neo-nazi beliefs)
  • Rebutted by Lynn on race differences in sexual behavior
  • Rebutted by Zuckerman on race differences in behavior
  • Rebutted by Allen (biologist) on genetic indexing of race groups
  • Rebutted by Cernovsky on race differences in brain size
  • Rebutted by Mouat on race differences in behavior
  • Rebutted by Tucker on legitimacy of Pioneer Fund’s hot takes
  • Rebutted by Fairchild on a number of Rushton’s takes
  • Rebutted by Flynn on ice age theory
  • Rushton was even rebutted (by Winston) on his claims that he was being targeted because of his ideology
  • A critique of Rushton and Templer’s 2012 paper
  • Refuted by Revelle and Wilt on claims regarding personality
  • Twinning and the r/K reproductive strategy: a critique of Rushton’s theory
  • “I do not believe that we should dignify this man and his ideas in public debate.… There will always be Rushtons in the world. We must be prepared to root them out.” -David Suzuki

Emil Kirkegaard

• Far Right, eugenicist, global warming denier, anti-feminist, abliest, transphobe, promotes white supremacy
• Has had multiple journals discredit his work, so now publishes his pseudoscience in Psych journal
• “The OpenPsych journals have no formal review process, are mainly reviewed by the small circle of Pioneer Fund associated researchers (Saini, 2019), and are not indexed in any bibliometric databases, but are used to disseminate work by Kirkegaard and others not fit for legitimate venues.” (Bird 19)(Saini 19)
• SPLC monitors him among others: https://www.splcenter.org/hatewatch/2018/03/12/wikipedia-wars-inside-fight-against-far-right-editors-vandals-and-sock-puppets
• owns Mankind Quarterly, a racist pseudo journal rejected by mainstream science. https://kevinabird.github.io/2019/12/18/The-Genetic-Hypothesis-and-Scientific-Racism.html
• his only qualification is a BA in Linguistics from Aarhus University.
• Regarding some of his quotes:
  • “Global warming pretty good for Denmark, yes, unless that Gulf Stream thing is real. I saw someone claim it isn’t. I didn’t investigate in depth.”
  • “Sexism is true. Stop thinking in isms. Think in numbers.”
  • “Dysgenics is real. Eugenics or Western civilization dies. Choose wisely.”
  • “When ratwiki[sic] can’t figure out whether something counts as anti-Semitic[sic] or not. Everybody knows that Jews are highly over-represented in creative wordy stuff. Hollywood Jews is an obvious term for those in that particular area.”

Bo Winegard

• Background:
  • https://rationalwiki.org/wiki/Bo_Winegard
  • https://www.mcclernan.com/2018/01/quillette-bell-curve-race-and-winegard.html
  • https://uncommongroundmedia.com/quillettes-attempt-legitimise-race-science/
  • https://www.thenation.com/article/archive/quillette-fascist-creep/
  • https://www.academia.edu/39171070/Steven_Pinker%CA%BCs_right-wing_alt-right_and_hereditarian_connections_version_3?email_work_card=title
• Rebuttals:
  • https://altrightorigins.com/2019/06/05/undead-race-science/
  • https://rationalwiki.org/wiki/Bo_Winegard#Human_Biological_and_Psychological_Diversity

Charles Murray

• BELL CURVE REBUTTAL: Block 96 Ned Block’s rebuttal of Bell Curve. https://www.nyu.edu/gsas/dept/philo/faculty/block/papers/Heritability.html
  • Review over the critiques of Stephen Gould’s response to the book
  • Comprehensive break-down of flaws in the book
  • Reviews over critiques of the Flynn Effect
  • Responds to Jensonian, Rushtonion, etc… arguments
• Roberts 04: “How Uncivilized! Reconfiguring Narratives of Innateness in Murray’s Human Accomplishment.” https://journals.sagepub.com/doi/pdf/10.1177/147470490400200111
  • Example: China
    • “He does, for example, spend considerable time and space acknowledging the contribution of China to world science and culture. But, in the end, the Chinese contribution is considered inferior to that of the West. Why? Basically, the Chinese were never able to measure their science in terms of a “framework that would enable the accumulation of scientific knowledge.” (Roberts 04 [pg. 59])
    • Murray does not account for certain characteristics of China that are only relevant with western science. One of these was the effective use of long distance communication. China was for most of its history a vast isolated country, divided into numerous districts and provinces, each having its own forms of governance. Communication was thus not in any way uniform or, in many cases, even existent. That a scientist working in Western China, let alone a lay-person, would know of, record, or comment upon the discovery of another scientist working in an eastern province was highly unlikely
  • Now it is clear that without frameworks, Murray does not count the achievements of certain civilization’s achievements. This is shown in his limited coverage of Africa: Murray claims the African Art was merely decorative or functional, and so excludes it.
    • “Of course, the claim—that Africans merely produced “functional objects”—is patently false: they produced structurally complex and aesthetically striking art objects, including both conventional and monumental sculptures, and numerous other purely aesthetic items that profoundly influenced Western European art from the mid-nineteenth century onward. But to maintain the exclusivity and centrality of “objective standards” for assessing accomplishment, Murray must regard all of African art as devoted to creating basic utensils, just a knife and fork kind of culture, and therefore entirely lacking the intellectual “framework” necessary for cultural accomplishment.” (Roberts 04 [pg. 60])
  • “Oh, but 97% of inventions were invented by white Europeans!” This statistic was, once again, blatantly misconstrued by Murray.
    • “First of all, Murray attributes absolute statistical certainty regarding European and North American accomplishment to his own compilation of scientific inventories. As we have seen, his compilation is biased from the outset, secreting a long-standing predisposition about race, sex, class and achievement. Moreover, as Judith Shulevitz, in her New York Times review of Human Accomplishment correctly argues, written scientific inventories were infinitely more common to Europe and North America than to China, the Far East in general, Africa, the Mideast, South America, or the various island civilizations. 15. And to argue, as Murray does, that the fact that inventories did not exist indicates that accomplishment in the arts and sciences in non-European cultures was meager, is patently absurd. The only reasonable conclusion that one can draw from the fact that inventories do not exist is that inventories were either lost, unaccounted for, or, more likely, were just not made. In short, the non-existence of a collection of biographical entries says virtually nothing about whether important scientific and artistic contributions existed in a given civilization.” (Roberts 04 [pg. 61]).
    • Overall Use of Statistic: “The conservative penchant for the inevitability of absolute—read, white male— authority is also addressed in Human Accomplishment. In this regard Murray pays special attention to the extreme differences between ordinary mortals and the 4,002 recorded geniuses, even going so far as to quote a passage that compares the ordinary with worms in face of some of these remarkable men. These giants, Murray argues, are the result of a “magnificent inequality” that is wholly quantifiable, and therefore an indisputable fact. But the “magnificent inequality” is wholly the invention of Murray, and, in this case, used as a means of justifying a set of social relations, which, in reality, are infinitely more complex than Murray leads us to believe. Social and intellectual ranking are largely the result of extraordinarily intricate socioeconomic, political, cultural, and historical conditions and relations, not the stipulations of conservative ideology. Moreover, one just might not feel worm-like or have the irresistible urge to prostrate oneself before such giants as Alfonso X of Castile, Karl L. Immerman, Antonis Mor van Dashorst, William McCune or C. H. D. Buys-Ballot, earth scientist.”
  • Sternberg 05
    • Intelligence, Race, and Genetics:(A refutation of multiple Race Realist scientists.)
  • Conley & Domingue 16
    • debunking 3 of HMs (Herrnstein-Murray) claims from The Bell Curve using modern methods
  • Young 20
    • evidence to support the study above: This article examines whether meritocracy is an effective device for legitimizing socioeconomic inequality.

Arthur Jensen

• Father of the modern Hereditarian movement
• SPLC classifies him as a White Nationalist (SPLC)
  • In interview with Jared Taylor of the American Renaissance, they agreed on:
  • the reduction of black birth rates, but non-reduction of white birth rates
  • support of eugenics and the pseudoscientific idea the black-white IQ gap is predominantly genetic, rather than socio cultural environmental
  • the right-wing conspiracy theory cultural Marxist “egalitarians” have taken over academia and suppress opposing viewpoints.” (Interview Link)
  • Sat at the editorial board of the Neo-Nazi, Neue Anthropologie (NA) (SPLC)
  • Financially supported by the white supremacist Pioneer Fund (SPLC)
• RACIAL ARGUMENTS:
  • He argued that “[t]he possibility of a biochemical connection between skin pigmentation and intelligence is not totally unlikely”, while this has never been proven -Rebutted on Race, IQ, Definitions, etc… by Suzuki & Aronson 05 https://www.researchgate.net/publication/232597552_The_cultural_malleability_of_intelligence_and_its_impact_on_the_racialethnic_hierarchy/link/09e41512279a54fbf9000000/download
  • Rebutted by Mackenzie 1980 on the substantiating of claims Jensen made in 1973 https://link.springer.com/article/10.1007%2FBF01066273
  • Jensen’s strawman: “High within-group heritability cannot prove between-group heritability, but it does increase the a priori likelihood of finding genetic components in the average difference between groups.” (Jensen 1973).
  • Response: All scientists are not against the possibility group differences in IQ are negligibly (1-5%) caused by genes, which is above zero, but most of these scientists are against hereditarianism.
  • “Many researchers who are primarily interested in environmental differences associated with racial and ethnic differences in intelligence would not be at all perturbed by an ironclad demonstration that, say, 3% of the [black-white] gap is due to genetic differences.” (Hunt 10, pg. 434-435)
• Rebuttals:
  • Rebutted by Nisbett 05 on misconstruing evidence, intervention programs and others http://www1.udel.edu/educ/gottfredson/30years/Nisbett-commentary-on-30years.pdf
  • Rebutted by Brace 1999 on his argument to make Hereditarianism the “Default Hypothesis”, on the basis that this is the null hypothesis https://www.journals.uchicago.edu/doi/pdfplus/10.1086/jar.55.2.3631210
  • JENSEN’S CLAIMS HAVE NO CREDIBLE SUPPORT:**
  • Friedrichs 1973: “In 1973, 341 American Psychological Association members were asked whether they disagreed or agreed with Jensen’s statement (quoted above). The survey revealed 60% disagreed, compared to only 28% who agreed and 2% unsure.[13] There is therefore scientific consensus against hereditarianism despite claims to the contrary by hereditarians.”
  • Rebutted by Plomin & DeFries (the actual source is by Mackenzie 84) on accuracy of predictions bc2c0da82792d5fc32e03a5e8791b5181c35.pdf (semanticscholar.org):
  • “Plomin and DeFries (1980) reviewed a large body of modern data that jointly indicated that the broad heritability of IQ in contemporary Western populations is around .50, rather than the .75-.80 that Jensen estimated.”

Richard Lynn

• Named a white supremacist by the Southern Poverty Law Centre
  • https://www.splcenter.org/fighting-hate/extremist-files/individual/richard-lynn
• In 2002, Lynn proposed that IQ differences determined economic development, not the other way around, therefore implying that people in Sub Saharan Africa are simply naturally dumber than people in Europe
  • [http://risk-resilience.berkeley.edu/sites/default/files/journal-articles/files/psychopathic_personality_and_racial-ethnic_differences_reconsidered_-a_reply_to_lynn_2003.pdf](http://risk-resilience.berkeley.edu/sites/default/files/journal-articles/files/psychopathic_personality_and_racial-ethnic_differences_reconsidered-_a_reply_to_lynn_2003.pdf)
  • https://www.researchgate.net/publication/223737846_Are_there_racial_and_ethnic_differences_in_psychopathic_personality_A_critique_of_Lynn’s_2002_racial_and_ethnic_differences_in_psychopathic_personality
  • https://academic.oup.com/esr/article-abstract/19/4/411/496362?redirectedFrom=PDF
  • https://www.sciencedirect.com/science/article/abs/pii/S1053535713000991
  • https://link.springer.com/article/10.1023%2FA%3A1020704322510
• In 2006, Lynn went explicitly racist and claimed that there is a genetic base for racial differences in IQ
  • https://www.sciencedaily.com/releases/2010/01/100121155220.htm
• He claimed Asians have shorter dicks than whites, and blacks have longer dicks than whites
  • https://www.psychologytoday.com/intl/blog/unique-everybody-else/201210/the-pseudoscience-race-differences-in-penis-size
• Although not to do with this section, he claimed that men are smarter than women
  • https://amp.theguardian.com/education/2005/nov/06/research.gender
• Cornoldi 10
  • Lynn inflated the IQ differences between south and north Italians e.g. see The mean Southern Italian children IQ is not particularly low

Neven Sesardić

• Sesardic believes: “the biological notion of race [is not at all inconsistent] with what the best contemporary science tells us about human genetic variation.” (Sesardic 10)
• Taylor 11
  • Sesardic’s claims are not enough to rehabilitate the biological concept of race
• Hochman 13a
  • Sesardic is wrong and science supports the social constructivist argument about race
  • See Spencer 2014, however (realize Spencer’s claims do not necessarily refute the crux of the argument. Also Hochman 2018 posted later continues the argument in favor of “popping” race instead of deflating it as Spencer supports)
• Hochman 13b
  • “In this paper I argue that Sesardic equivocates between two versions of racial naturalism: a weak version and a strong version. As I shall argue, the strong version is not supported by the relevant science. The weak version, on the other hand, does not contrast properly with what social constructionists think about ‘race’. By leaning on this weak view Sesardic’s racial naturalism intermittently gains an appearance of plausibility, but this view is too weak to revive racial naturalism”
• Hochman 18
  • Deconstructs Sesardic’s response to Hochman 13b and a
  • Proves that through contradictions and even citing counter-evidence, Sesardic disproves his own claims on race
  • Also attacks Sesardic’s strawmanning of other academics (Gould, Marshall, etc…)
• Pigluicci 13
  • General overview of Sesardic’s claims and why they are false/don’t make sense/strawmanning positions
• Tabery’s Response (2015)
  • Points out contradictions of Sesardic and how he can’t read (Sesardic critiqued something Tabery had already addressed)
  • Points out the flaws in Sesardic’s argument that Lewontin’s marxist views undermine the latter’s thesis
    • “What I refuse to take seriously is the possibility that Lewontin’s politics explain why Hogben (who was not a Marxist) made the same arguments concerning the importance of interaction three decades before Lewontin, or why Moffitt and Caspi (who are not Marxists) continue to make the same arguments concerning the importance of interaction three decades after Lewontin. If Sesardic wants to offer up political bias as an alternative hypothesis for the persistence of the interaction debates, then further documentation of Lewontin’s politics is just a distraction; in contrast, he must show how the same political biases shaped the varied advocates of interaction across the three controversies.”
  • BONUS INFO: attacks Munafo on his claims that GWAS is better than cGxE (completely unrelated to Sesardic)
• Response to specific arguments in Sesardic 05
  • Lewontin’s garden:
    • Hereditarians want to achieve: High WGH, together with some collateral empirical information, inductively establishes a non-zero BGH.
    • Mackenzie 1980, Mackenzie 1984
  • Further, Jensen constructs a strawman:
    • “Many researchers who are primarily interested in environmental differences associated with racial and ethnic differences in intelligence would not be at all perturbed by an ironclad demonstration that, say, 3% of the [black-white] gap is due to genetic differences.” – Earl Hunt, Human Intelligence. (2010). Cambridge University Press. pp. 434-435.
    • The data that sesardic presents to portray nonzero BGH is not the same as the 0.5 or .8 argued by Jensen, Rushton and Lynn. The burden falls on them to prove HIGH BGH not simply nonzero so that scientists entertaining the idea of IQ differences as a result of genetics at some negligible number are not painted as hereditarians

Donald Templer