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As the Basis of Prejudice or Bias, Not Race But Sex (Gender) Is Salient and in the ‘Wrong’ Direction—Critical Social Justice Theory Identity Politics Is Debunked

Submitted:

17 March 2025

Posted:

18 March 2025

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Abstract
There is no empirical support for and instead profound evidence against the core tenets of Critical Social Justice Theory identity politics. Review of a number of very recent comprehensive studies of negative attitudes shows race is barely salient as its basis, and whereas by contrast sex (gender) is highly salient it is in entirely the opposite direction to that presumed: anti-male and pro-female, not pro-male or anti-female. This is congruent with earlier research and the closely related investigations of homophily, and attested by majority male victimhood across hate crime domains. There is deep biological theoretical foundation to these findings in reproductive imperatives, with anti-male bias mediated by male hierarchy, leaving now hegemonic ideology absent theoretical cogency as well as counter-evidenced.
Keywords: 
Critical Social Justice Theory (CSJT)
;  
negative attitude
;  
bias
;  
sex
;  
race
Subject: 
Social Sciences  -   Behavior Sciences

Very Recent Study of Negative Attitudes Overturns Ideological Assumptions

The most comprehensive, in-depth, robust set of psychology studies to date investigating the basis of implicit bias / prejudice (default psychological negative attitude) (Connor, Weeks, Glaser, Chen & Keltner, 2023) confirms previous research and is in line with several other new studies discussed herein, showing that target race is weak and inconsistent (of low salience), whereas target sex (gender) clearly trumps all other factors, with effects many times larger than any other; and this across all modes of measurement, where sex also explained more variation than any other factor. Instead of the supposed mutual compounding of factors (as in the oft-used but non-scientific notion of intersectionality), it is more the case that there is category dominance of sex, and profoundly so.
That sex is the sole significant predictor of bias (in comparison with race and other putative factors, when subjects are left to decide as to which category they would attend) was first found by Jones & Fazio (2010). More recently, the category dominance of sex is the overall finding of numerous studies (all discussed forthwith), notably a review by Petsko & Bodenhausen (2019). Furthermore, the bias Connor et al. uncovered is not in the usually assumed uniform direction but is “counter-intuitive”: instead of bias being anti-female it is instead anti-male. [Note this would not be against the intuition of many or most ordinary people, who are not (or are far less) infused with elite ideology.] The most consistent other factor, albeit relatively weakly apparent, is social class, such that, however sampled, implicit bias is robustly both generically anti-male and specifically anti-lower-class-men; and not just pro-female but most particularly pro-upper-class-women:
“the most negative implicit evaluations consistently (were) made of targets displaying multiple intersecting stigmatized social identities (in this case, lower SES males), and the most positive implicit evaluations (were) made of individuals displaying multiple intersecting positively-valued social identities (in this case, upper SES females)” (p51).
Independently, Steele & Lipman (2023) find and confirm the primacy of sex (gender) over race and, furthermore, the inversion of the usual assumption that bias is against the female. Non-black children aged five to twelve years given a choice of whether to categorise others in terms of sex or race do so in terms of sex and not race, and if asked to categorise by sex prefer (show more positive associations with) own-sex (-gender) black targets over other-sex (gender) white, though boys much less than girls expressed an own-sex preference. Still further confirmation comes from Bai & Campos (2022), in their finding that in respect of the intersection of race and sex (gender):
“... the interaction term is significant, but in the opposite direction implied by the multiplicative perspective. This again counter-intuitively suggests that minority women experience stigmatization less than what would be expected from linearly combining the expected stigmatization experience from minorities and women”.
Race, in marked contrast to sex, is found by Yang, Yang, Guo & Dunham (2022) to have so little salience in the in-group preferences of young children as to be eclipsed by or is on a par with even the minimal group condition in experimental settings. Simon & Gutsell (2020) make the same finding in respect of race even when race is made particularly salient, and they review a literature of similar findings. In another comprehensive set of studies, Misch, Dunham & Paulus (2022) also find that sex is a profound basis of bias in contrast to race, concluding that:
“... the developmental trajectory of gender group bias looks very different from the developmental trajectory of racial groups: gender group bias is stronger and much more stable over time. Other studies in the past have found similar fundamental differences between gender groups and other group types. Children’s gender group bias does not only appear earlier and stronger (Dunham et al., 2011; Katz & Kofkin, 1997), but children assign more meaning to it (Rhodes & Gelman, 2009), and are not susceptible to cooperative cues which trigger group bias in other group types (Misch et al., 2021).”
... young children already showed a much stronger bias in gender groups compared to racial groups, and gender-related bias remains relatively stable as children grow older. This finding dovetails nicely with previous research showing that children recognize and encode gender earlier and more reliably than race (Katz & Kofkin, 1997; Weisman et al., 2015) and also perceive gender categories as less flexible and more meaningful than racial categories (Rhodes & Gelman, 2009).”
The authors opine that: “... it is possible that group membership based on gender triggers different cognitive and social identity processes than other types of groups, perhaps because racial groups but not gender groups are interpreted as potential cooperative coalitions (Kurzban et al., 2001).” Varying the basis of categorisation of targets, Yamaguchi and Beattie’s (2019) experiments reveal little implicit racial bias when sex is the basis of categorisation. Implicit bias here is pro-female and anti-male. It is only when race is made profoundly salient through categorisation on that basis that there is any anti-black and pro-white implicit bias. Nosek & Banaji (2001) previously likewise manipulated the salience of race and sex. Albeit in the race-salient condition a bias in favour of white males over black females was apparent, instead making sex salient reversed this bias. Examining victimisation by both acute and chronic forms of discrimination, Veenstra (2013) finds “high levels of discrimination both in terms of race and sex reported by men are at odds with the additive and intersectionality-inspired perspectives which accord women the gender identity most vulnerable to discrimination”. That negative attitude towards blacks is mostly against males has been shown by a considerable number of studies; eg, Seaton et al., 2008; Sellers & Shelton, 2003; Garcia Coll et al., 1996. It’s revealed not only in young (four-year-old) children (Perszyk, Bodenhausen, Richeson & Waxmanet, 2019), but even in three-month-old infants, in own-race over other-race faces being preferred only when the faces shown are male (Ziv, 2012), confirming previous studies. Bias here is mostly towards black boys, followed by white boys, then black girls, indicating it is primarily being male, not being black, that is operative. The extremely young ages of subjects in these studies precludes the possibility that socialisation could be responsible. Taking together all of these investigations over the past quarter-century indicate an implicit,evolved basis of negative attitudes being not only primarily in respect of sex, with race only a secondary factor, but towards male, not female targets.

Further Insights from the Connor et al Study Set

Remarkably, across Connor et al.’s series of studies, “target race in general tended to produce relatively inconsistent effects compared to target gender and social class, regardless of the measurement method” (p 55). Indeed, there was “little evidence of anti-black bias … if anything, we observed weak evidence of anti-white bias” (p26). In an additional, particular mode of measurement—the AMP [affect misattribution procedure]—Asian and black targets were favoured over white. Only when in effect the measure was a forced choice procedure, in the case of the original, basic two-category (black/white) measure of implicit bias, did the usually assumed pro-white/anti-black bias emerge. Removing forced choice reveals the reality of bias with respect to race:
“Asian participants displayed a clear in-group bias favouring Asian over black and white targets, black participants favoured Asian and black targets over whites … a pro-Asian/anti-black/anti-white bias was detected via the EPT [evaluative priming task] data. … Latino participants favoured Asian over black targets, and white participants displayed no significant racial bias overall” (p46).
This too is counter-intuitive in being completely at odds with received opinion, but should not be surprising from an understanding of in-group/out-group psychology (that there is primarily in-group affiliation and favouring rather than out-group disafilliation and derogation). That ethnocentrism is nothing more than in-grouping is shown by Hales & Edmonds (2019), who further find this may or may not (at all) be based on ethnic markers, and instead can be from any kind of in-group marker, and that this can be quite fluid. It is no surprise, then, that implicit bias data in respect of race is highly inconsistent. Minority ethnic populations might be expected to possess a stronger in-group bias in comparison to the majority (host) population, and albeit that in-group bias is in-group favouritism rather than out-group hostility (as well established in the literature), the latter would be expected to arise as a derivative of the former, detectable in terms of bias.
To compare with Connor et al. a literature search reveals almost no other recent research on anti-white racial implicit bias—any of implicit racial bias in both directions—presumably because of the ideological insistence that racial bias must be considered unidirectionally white-to-black. Not a single pertinent paper is listed on the Project Implicit website, but this omits that by Morin (2015), who found black and white participants displayed in almost identical proportion pro-own-race /anti-other-race (almost 50%), pro-other-race / anti-own-race, and no preference (each about half of the remaining 50%). For Asians, those in the first two categories were each about 40%. Morin’s research also undermines the conventional narrative, albeit not as pointedly as does Connor et al. Not only is race completely trumped by sex as the basis of implicit bias, but it appears that any impact of race actually is an aspect of sex (Galinsky, Hall & Cuddy, 2013; Johnson, Freeman &Pauker, 2012; Goff, Thomas & Jackson, 2008). The major racial groupings of Caucasian, Asian and African differ markedly, with Asians possessing a small body frame, light muscularity, a delicate-featured face (also, a meeker demeanour). The African body is notable for being the opposite on these criteria, with Caucasians somewhere in-between. Sub-divisions of these major racial groups make for a range, so that race seems not to be a phenomenon of discrete entities but differences in degree: a continuum. This is evident in how we implicitly view race in neuroscientific experiment (Gwinn & Brooks, 2013).The likely basis of this racial differentiation is by extension from the human species having evolved through neotenisation (paedomorphosis) of an ape common ancestor, as hypothesised for Asians (eg, Bromhall, 2003; Montagu, 1989). Asians in this view are especially neotenised humans. The extent of neoteny/paedomorphosis is the extent of maturity (adult appearance). The more neotenised, the more immature (that is sexually immature) is appearance, so sex is likely to decrease in salience with increase in neoteny.
Consequently, target sex would be the factor determining the direction and level of implicit bias according to racial category as well as whether male or female.
Given implicit bias in terms of sex is shown to be pro-female and anti-male, then bias additionally in terms of race will interact with that in terms of sex, still further pushing implicit bias in the pro-female and anti-male direction. A picture more antithetical to Critical Social Justice Theory (CSJT) identity politics would be hard to imagine, and it’s robust. The Connor et al. research is ground-breaking in being the first to measure in combination implicit bias towards individuals (target individuals rather than groups) in terms of sex, social class, race and age—the known or suspected key factors, with all being systematically varied against each other. In this way all possible models of the basis of implicit bias are fully tested. Connor et al.’s series of sub-studies successively better controlled and otherwise strengthened experimentation to ensure any aspects of the research that may be problematic were addressed.

Empty, Non-Scientific Riposte To Connor et al

With its profound debunking of the basis of CSJT identity politics, not least its key notion of intersectionality, an ideological, scientifically empty or anti-scientific riposte to Connor et al. would be anticipated. Chamberlain, Holroyd, Jenkins & Scaife (2023) duly provide this, claiming that a proper conceptualisation of intersectionality is of factors not compounding quantitatively (either additively or multiplicatively) but combining in somehow “qualitatively distinct experiences of oppression” (p8). This previously had been similarly proposed, by Liu & Wong (2018), after failing to find support for any of several hypotheses, but they concede that nothing within their intersectional fusion syndrome (of uniqueness of particular intersections that cannot be gauged from the components) can be operationalised into a measure. Neither set of authors are able to describe or envisage what or how this could be, in what is non-explanatory special pleading to support a not even conceivable putative phenomenon. All possible interactions are covered by Connor et al.: either there is compounding of some kind or one category is more or less dominant. Even if there could be and there were some other form of compounding, this would be empirically evident in Connor et al.’s data, which it is not. Contradictory results within some of the data are pointed to by Chamberlain et al. as if this would somehow support their critique, when it is simply evidence against any multi-factorial understanding to leave sex key. They go on:
“there are strong racial associations with class: black people are more strongly associated with ’poverty’ (a key component of class status) (Brown-Iannuzzi et al., 2019; Cox et al., 2015). Either kind of influence would undermine the category dominance hypothesis. This is highly relevant to how we interpret the results, but the experimental tools used don’t appear to be able to speak to it”.
These are false claims. Correlation as ever does not indicate causation. The very well-documented distinct father absence among US blacks is well understood to mediate poverty and low class. Connor et al. developed eminently suitable experimental designs, answering any deficiencies they discovered as they arose in the course of their series of experiments. Whether or not category dominance is either undermined or supported is the very question their research is designed to uncover. Chamberlain et al. also disingenuously claim there is: “an assumption that the interactions between biases will be uni-dimensional. This precludes the possibility that a qualitatively different kind of bias might be expressed”. Yet whatever the nature of any category, if it interacts with another category then this will be apparent quantitatively if the interaction is of any salience. A mechanism of interaction is a separate question. Inasmuch as the IAT (the standard test of implicit association) has deficiencies, this applies not least to measuring racial and sex-based bias as claimed in CSJT identity politics itself. So Chamberlain et al. are here attacking the latter’s very basis in their utilisation of it as the foundation of their attempt at a critique.
The detractors of Connor et al. reveal their hand on page 15 in talking of “problematic data”,when this is not a notion within science. Data is data: all is useful in either supporting or undermining an hypothesis, and supporting (or not) either an alternative or the null hypothesis. Only if what is seen to be at issue is the undermining of a firmly held ideological stance is data deemed problematic, and ideology is no part of science. A further complaint completely boomerangs: “the problem of assuming that all members of a social category have similar experiences of discriminatory treatment” is the very problem at the heart of CSJT identity politics and intersectionality, not of their examination in experimentation. Chamberlain et al. do concede that “... we do not make the strong claim that no quantitative measurements are useful for understanding intersecting biases. Nor do we at this stage recommend against the use of quantitative implicit measures such as the IAT”. [p21].
They hardly could do otherwise, as then there would be no means of investigation. No suggestion is proffered either as to how measurement alternatively might be carried out nor the nature of what it is they would be seeking to measure. Connor et al. have made no reply to Chamberlain et al., because (they indicate in personal communication) the latter’s paper is completely empty of any scientific challenge, leaving nothing for any riposte to address.

Salience of Sex in Interaction with Race Is Apparent in Hate Crime Data

The supposedly unexpected interactions of race and sex found in the several very recent sets of studies by different research teams are notably apparent in hate crime statistics in respect of the sex of victims in the domain of race: 71% male in the one major comprehensive UK study to date (Walters & Krasodomski-Jones, 2018) using Metropolitan Police Service (London) data. In England & Wales the CPS (Crown Prosecution Service) release the police-recorded (pre-charge) statistics quarterly. Uniformly across every quarter and for every year since data collection began, up to and including the present, the majority of victims substantially are male.
In the latest available (quarter 2 of 2024-2025: Table 3.3 of the CPS Data Summary Quarterly Update), there were 3,116 female victims but substantially more male: 4,921 (61% of the total). Additionally, the sex of 976 victims was unrecorded. For comparison, the 2016-2017 figures are similar: 2,850 female and 5,368 male (65%), with 3,636 unsexed. Note that the quite large unsexed proportions would further skew the male predominance of victims if the sex of these victims could be ascertained, as likely these will be predominantly if not overwhelmingly male, given that foundational to the notion of hate crime (as very well understood by police and the public) are CSJT identity politics and intersectional imperatives, with the protected characteristics of race and sex being meant to be interpreted as unidirectional prejudice of male to female and white to black. Still further, the sex differential will be heavily understated given sex differentials in under-(and over-)reporting. This has not been researched specifically in respect of hate crime, but for crime generally male comparative under-reporting is the principal predictor of the likelihood or not of reporting a crime (Avdija & Giever, 2012). It is highly indicative that even regarding violent assaults, in marked contrast to women “men victimized by strangers most often do nothing” (Kaukinen, 2002). This is in line with the reluctance of males compared to females to seek help in any context where it has been investigated (Vogel & Heath, 2016; Yousaf, Grunfeld & Hunter, 2015; Möller-Leimkühler, 2002), including even when suicidal (Rasmussen, Hjelmeland & Dieserud, 2018). Conversely, there is good reason to suspect a major sex differential in the opposite direction in regard to over-reporting, given that vulnerability confers sexual attractiveness to females (Goetz, Easton, Lewis & Buss, 2012; Rainville & Gallagher, 1990), whereas for males it undermines status, thereby instead reducing their sexual attractiveness.
Male comparative under-reporting and female comparative over-reporting would pertain to both police-recorded incidents and those discovered in survey, and there is survey data re hate crime collected in the CSEW (Crime Survey of England and Wales). However, the Home Office is not releasing it until 2025 (three years after the resumption of face-to-face interviews post-COVID), on the stated grounds of a need to aggregate all of the data over a three-year period because of small sample sizes. As small sample size is also stated to be a problem for CSEW rape data yet has never precluded its release, then the hate crime data appears to be withheld for another reason not to do with data quality. A suspicion must be that its release would undermine standard narratives re prejudice and the sexes. The very recent sets of studies by Connor et al., Yang, Yang, Guo & Dunham, and Steele & Lipman, and Bai & Campos build on a body of research on racial and gender attitudes, previously reviewed by the present author respectively in 2019 and 2018; further confirming the overall conclusion that as the target of bias/prejudice, not race but sex is salient, and males rather than females.

Looking Specifically at Sex, Implicit Bias Is Pro-Female and Anti-Male

Turning now from the salience of sex over race to look in more detail at the further discovery that implicit negative attitudes are towards not females but males, most recently this has been shown by Koda, Tsuji & Takase (2022) using virtual male and female agents as targets. They find “a general gender bias against the male agent that is not related to sexual roles, and this tendency was more pronounced among the female participants”. This reality is recognised despite heavy indoctrination to pretend the reverse: “both male and female university students are more concerned about the impact of gender bias against men“ (Esperanza, 2020). Most comprehensively, in a review of all studies together with their own new studies, Dunham, Baron & Banaji (2016) find that across all age groups, boys/men had “no negative association with female whatsoever” (p5). On the very contrary, males had a strongly positive attitude towards females from adolescence onwards,and even as boys have little comparative same-sex preference. Likewise, girls/women across all age groups are “robustly pro-female”, and increasingly so with age; that is, unlike males, females have a strong same-sex bias. The robustness of male pro-female bias extends to conditions that might be expected to nullify or reverse it. Even when males are under threat to their sense of self-worth, making sex salient still does not prompt males to have negatives associations with women (Ishii & Numazakihad, 2015), including in the particular scenario of women entering the workforce. Brown & Cotton (2015a, 2015b) dismiss a contrary claim in this regard by Kasumovic & Kuznekoff (2015) as having no statistical significance once appropriate statistical analysis is used. Female pro-female attitudes together with male absence of sex preference is found in experiments with humanoid robots (Tung, 2011).
Less recent investigation similarly has found strong pro-female bias by both males and females, albeit not as strongly by males as by females themselves (Carpenter, 2001), or present though non-significantly by males (Skowronski & Lawrence (2001). On only one type of measure of implicit bias was even a slight pro-male attitude found. The authors had to introduce a very strong demand characteristic (turning targets into soldiers) to elicit pro-male as well as pro-female bias. A neutral sex bias by males was found by Aidman & Carroll (2003). Still earlier research all found universal (male as well as female) pro-female bias (Haddock & Zanna, 1994; Eagly, Mladinic & Otto, 1991; Eagly & Mladinic, 1989). Eagly and collaborators were pioneers in using a range of direct measures (rather than the indirect measures employed in previous studies that the authors considered conceptually and methodically flawed), and found pro-female attitude to be solidly in terms of both beliefs or emotional response. Even in their probing for hidden negative or ambivalent attitudes, either cognitive or affective (emotional), none were found.
A very closely related body of evidence—if not a measure of the same phenomenon—is that concerning preference for others as fellow in-group members (automatic in-group bias) on the basis of the major shared category of sex: sex homophily. This was found by Nosek & Banaji (2001) and also by Richeson & Ambady (2001) to be profoundly strong for women yet completely absent in men—that is, men have no preference at all for their own over the opposite sex. Nosek & Banaji conclude (p652): “Replicating previous work on gender attitudes, subjects showed an automatic preference for females over males. Also as observed before, women showed a strong preference for females over males, men showed only a slight preference for their group, and the subject gender difference was significant and strong. Reassuringly, these results replicate previous work using the IAT procedure to test race and gender attitudes (see Carpenter & Banaji,2000; Greenwald et al., 1998; Mitchell, Nosek & Banaji, 2001; Nosek, Banaji & Greenwald, 2002).”Rudman & Goodwin (2004) quantified the female same-sex preference as five-fold, and this by a more accurate measure of implicit attitude that removed any confound with gender stereotypes. This is despite females being exclusionary in their social behaviour towards other females (Benenson et al., 2013; Goodwin, 2002); that is, females are highly selective as to with whom they associate among females. With a five-fold same-sex preference notwithstanding, then the bias against males must be profound indeed. Rudman & Goodwin conclude that as regards women: “they alone possess a cognitive mechanism that promotes own group preference” (p506). That this is not culture-specific is shown by the replication of these findings in a Japanese sample (Ishii & Numazakihad, 2009). Unlike women, men are found to regard everyone, irrespective of sex, as being fellow members of any symbolic grouping (however minor the basis of affiliation) to which they themselves belong (Maddux & Brewer (2005), David-Barrett at al (2015), Szell & Thurner (2013) and Lindenlaub & Prummer (2013) all find similarly. The most recent study of sex homophily (Pignolet, Schmid & Seelisch, 2024), looking at Instagram postings, finds that “women interact with each other more than twice as often than with men, while men show only a light preference for interactions among themselves.“ Using a new methodology of examining identification with fictional characters, Hook (2019) finds that females identify more strongly with their own gender whereas males identify equally with either gender. Investigating on-line twosomes and threesomes, Laniado, Volkovich, Kappler & Kaltenbrunner (2016) conclude: “... we find evidence of a strong homophily for women, and little or no homophily for men.”
Further insight on anti-male and pro-female bias is provided by data on the interaction between sex and sexual orientation, which is examined in some detail in the present author’s 2019 paper (pp26-33), to which the reader is referred, as it does not require significant updating here. As with the interaction between race and sex, hate crime data shows a substantial majority of male victims (pp26-27). A large set of studies reveal that homophobia (more properly, homonegativity) is specifically towards male, not female homosexuals (and bisexuals; also trans-sexuals by birth sex) (pp27-31), though what is dubbed homophobia or homonegativity is a misnomer, being apparently a subset of much wider negative attitude towards males on the basis of being different in some way; homosexuality being the quintessential difference (pp31-33). This indicates or suggests that sex-differential or sex-dichotomous target negative bias is rooted in sex and reproduction.

The Deep Biological Basis of the High Salience in Implicit Bias of Sex and of This Being Anti-Male and Pro-Female

Anti-male and pro-female implicit bias/prejudice appears to have a deep biological basis in reflecting the sex dichotomy in function of the sexes necessary to deal with the core problem for all biological systems of the accumulation of gene replication error. [For an outline and literature review, see Moxon, 2019, and earlier papers.] The mechanism that has evolved is for individuals of one (the male) half of the lineage to assort according to relative genomic integrity (absence of deleterious genetic material), with individuals of the other (female) half of the lineage selecting as mating partners only those with relatively high genomic integrity. All those who remain unselected thereby fail to reproduce, and in consequence remove the deleterious genetic material they harbour from the local gene pool. Also contributing in this regard are those males who do reproduce but minimally, as through pair-bonding with low-fertility females.
This mechanism has been dubbed the genetic filter (Atmar, 1971) or mutational cleanser (West-Eberhard, 2005).In restricting (quarantining) the assortment to one half of the lineage allows the other half of the lineage to focus on reproduction. Given that the female, being the sex with the larger gametes, has evolved necessarily to be encumbered by gestation and lactation to render her the limiting factor in reproduction, then the assortment mechanism cannot burden the female half of the lineage. It has to be the male side. With the male being capable of impregnating a large number of females, then relatively few males are required even for all females to reproduce and continuously, and consequently the assortment mechanism can consign most males to non-reproductive status or to have relatively less reproductive opportunity. This is the whole point of the mechanism. Not only does this not compromise overall reproductive output of the local reproductive group but maximises reproductive output in terms of quantity x quality; that is, it maximises reproductive efficiency, as it were. Quality of offspring in terms of absence of deleterious genetic material is vital for future reproduction, so is more important than mere quantity.
This appears to be the foundation of social system, as it can be seen to be the origin of all facets of sociality, notably male hierarchy. Both pro-female and anti-male implicit attitudes now make sense. All females, being the vessels to carry the filtered or cleansed genetic material of a minority of males to the next generation, are required to reproduce, with the only obstacle some severe compromise to their physiological fertility, which usually anyway would not be evident until continued failure to conceive or oft-repeated miscarriage. Hence the default attitude towards the female is positive. In marked contrast, only a minority of males are required to reproduce. Most males not only are not required to reproduce, but unless they are of the minority of males of high genomic integrity and thereby preferred by females, then in the interests of overall reproductive efficiency their sexual access needs to be curtailed if not eliminated entirely. Hence the attitude to males generically is by default negative, not positive. Only if an individual male can demonstrate absence of deleterious genetic material relative to other males might negative attitude towards them be attenuated or reversed. It appears that this has to be relative to more than just a substantial minority of males, and more than just most other males, but to a substantial or overwhelming majority of other males. The profound sex difference in reproductive output is revealed in DNA analysis: across human evolutionary history only 40% of men ever reproduced (Wilder, Mobasher & Hammer, 2004), as confirmed by Favre & Sornette (2012): “the present generation has 1.4 times as many female as male ancestors”. Furthermore, among this male minority is a huge reproductive skew, where “only a few men may have contributed a large fraction of the Y-chromosome pool at every generation” (Dupanloup et al., 2003). This is through polygyny, that as well as being ancestrally formal appears to be a de facto cultural universal given serial monogamy, parallel discreet pair-bonds, affairs, and iterated casual sexual encounters. More indicative today of ancestral male reproductive skew, given the contemporary range of contraceptive technology, is the skew in male sexual access, which is apparent in analysis of the data of on-line dating sites. OKCupid.com posted (since deleted) on the company’s blog in 2009 a study (OkCupid Checks Out The Dynamics Of Attraction And Your Love Inbox) revealing that their female clientele considered 80% of the male clientele as “below average” in attractiveness. By contrast, attractiveness ratings in the opposite direction formed a normal distribution curve, with men rating most females as being at least averagely attractive. [An 80/20 disparity was also found by Tinder but the sample size was so tiny that results are swamped by the error margin.] That here not even 40% but a mere 20% of males are in female consideration is likely through females using online dating sites to select not only for prospective pair-bond but also extra-pair sex partners, about whom females are more discerning. It appears that females form pair-bonds as a default mating strategy to secure acceptable quality offspring into the future (in effect projecting forward in time their peak reproductive value) but with the option of discreet extra-pair sex with males of significantly higher mate value than their pair-bond partners in order to conceive offspring of correspondingly higher quality, while retaining the pair-bond partner in a failsafe role to conceive additional offspring if extra-pair conception is not secured (Moxon, 2021).

Anti-Male Prejudice Is Mediated by Male Hierarchy

The assorting of males according to genomic integrity as the basis of the hierarchical sociality of males evident across species -- no less in the human case -- explains why dominance hierarchy across species is male-specific (Moxon, 2023). The need to police it explains why there is generic anti-male prejudice: to ensure low-ranking males are restricted in their sexual access, if not denied access altogether. It would be anticipated that there has to have evolved a default implicit bias against males, mitigated by high rank, and this is what is found (to reiterate) by Connor et al.: the most negative implicit bias of all was directed towards low SES males—the diametric opposite experience to that of high SES females—on top of males generically being the recipients of bias against them, which is not the case for females.
Violation (or cheater) detection mechanism to police low-ranking males in a dominance hierarchy has been discovered by Cummins (1996a, 2005, 2019), with the same or similar –detecting violations of rules relating to navigating hierarchies (submitting to authority) and maintaining coalitions so as to expose unhelpful individuals, traitors, and rebels—uncovered by Sivan, Curry & Van Lissa (2018). The cognition individuals employ here is of a type known as deontic reasoning: that regarding obligations, permissions and prohibitions; what one (and another)should or should not do according to circumstances. This is just what apply to individuals by virtue of membership of a hierarchy, depending on rank; and, indeed, to qualify as a member of the hierarchy in the first place (which is culturally encoded in male initiation ceremony). Cummins finds this violation detection is not evoked in other cognitive modes and is clearly a deep-seated evolved specific (modular) mechanism (Cummins, 1996b), already implicit even in early childhood (Cummins, 1996c; Harris & Nuñez, 1996). It’s modular and automatic nature is also found by van Lier, Revlin & de Neys (2013) and Bonnefon, Hopfensitz & De Neys (2013), with this position further consolidated by Cummins (2013). Bonnefon’s team notably find that males compared to females are seen as less trustworthy: negative bias by another name. It’s a mode of cognition activated more in respect of low-status individuals (Cummins, 1999), it is specifically targeting males of low status (Oda, 1997); and in particular by other low-status males (Fiddick & Cummins,2001). Furthermore, males of low status and deemed to violate their obligations, permissions or prohibitions—in other words, cheat—are seen as being unattractive (Mehl & Buchner, 2008; Bell & Buchner, 2009); that is, bias against them is in terms of a perception of sexual undesirability, just as is most apposite for a mechanism to conditionally deny sexual access.

The Non-Science of Microaggression

CSJT identity politics supposedly is manifest in microaggressions, but a slew of very recent reviews have comprehensively dismissed the concept (as currently formulated) as having no scientific basis. In a review of the literature Nadal (2023) concludes that it demonstrates the great difficulty in deciding what is and how to measure a microaggressiom, and of combatting challenge that the concept is scientifically empty. It’s circular, according to Syed in his 2021 review: predicated on the assumption of racism, and (as several studies cited as far back as 2008 indicate) completely failing to show intent. Cantu & Jussim (2021) see insuperable definitional difficulties among a plethora of problems, with no grounding in solid scientific methodology, instead being an attempt to “retroactively validate initial ideological hunches” (p222), leaving currently used lists of microaggressons “(un)representative of anything meaningful” (p264). Major concerns regarding conceptualisation are also concluded by Lui, Berkley, Pham & Sanders (2020) from their mixed-methods experiment to try to demonstrate the phenomenon, which revealed the central importance of context, and highly inconsistent responding in respect of ascribing intent. A most comprehensive review by Lilienfeld (2017) is damning about five key suppositions, that microaggressions:
“(1) are operationalized with sufficient clarity and consensus to afford rigorous scientific investigation; (2) are interpreted negatively by most or all minority group members; (3) reflect implicitly prejudicial and implicitly aggressive motives; (4) can be validly assessed using only respondents’ subjective reports; and (5) exert an adverse impact on recipients’ mental health. A review of the literature reveals negligible support for all five suppositions.
“… [The whole research programme] has been marked by an absence of connectivity to key domains of psychological science … and is far too underdeveloped on the conceptual and methodological fronts to warrant real-world application.” [Abstract] “The ... presumption that certain microaggressions are invariably or usually associated with widely shared implicit messages has yet to be investigated empirically; moreover, this presumption is at variance with large bodies of research and theorizing in social cognition and cognitive-behavioral therapy. The concept of unintentional microaggressions is oxymoronic, as it runs counter to traditional definitions of aggression. Furthermore, there is no evidence that microaggressions are statistically associated with aggression or prejudice in deliverers.” [p148]
Replying to his critics, who defend microaggression research, Lilienfeld (2020) points out that remaining unaddressed are key concerns of the absence of definition, the invalid methodology of exclusive reliance on subjective reports, and non-evidenced impact on mental health.

CODA

There is not only no empirical basis of Critical Social Justice Theory identity politics as a whole or in any aspect, but instead it is comprehensively undermined by evidence, and, furthermore, the counter-evidence is congruent with profound biological theoretical underpinning. Yet this is not apparent in the stance currently taken quite widely within academic psychology and related disciplines, which is an abuse of science through mis-framing and distortion in the effort of supporting what is a purely ideological notion, that, as such, axiomatically should have no place whatsoever in science. This is to the great discredit of academia, and has now been called out specifically re psychology by O’Donohue (2023) -- Prejudice and the quality of the science of contemporary social justice efforts in psychology; a chapter in the book Ideological and Political Bias in Psychology: Nature, Scope, and Solutions—more generally re social science by Abbink (2024), and re all science by Krylov & Tanzman (2023). The basing of government policy in CSJT amounts to wilful misconduct by officials and politicians, and is therefore open to legal challenge.

References

  1. Abbink, J. Preserving reason: A rationalist defense of the ‘Western telos’ in social science and the humanities. Kulturní Studia 2024, 23, 42–65. [Google Scholar] [CrossRef]
  2. Aidman, E.V.; Carroll, S.M. Implicit individual differences: Relationships between implicit self-esteem, gender identity and gender attitudes. European Journal of Personality 2003, 17, 19–36. [Google Scholar] [CrossRef]
  3. Atmar, W. On the role of males. Animal Behavior 1991, 41, 195–20. [Google Scholar] [CrossRef]
  4. Avdija, A.S.; Giever, D.M. Examining the effect of selected demographic characteristics on crime-reporting behavior. Journal of Alternative Perspectives in the Social Sciences 2012, 4, 790–821. [Google Scholar]
  5. Bai, H.; Campos, N. (2022, July 22). On Double Jeopardy: Race and Gender Explain Intersectional Subjective Stigmatization Likely Additively, and Possibly Projectively, But Unlikely Multiplicatively. Preprint. [CrossRef]
  6. Bell, R.; Buchner, A. Enhanced source memory for names of cheaters. Evolutionary Psychology 2009, 7. [Google Scholar] [CrossRef]
  7. Benenson, J.F.; Markovits, H.; Hultgren, B.; Nguyen, T.; Bullock, G.; Wrangham, R. Social exclusion: More important to human females than males. PLoS ONE 2013, 8, e55851. [Google Scholar] [CrossRef]
  8. Bonnefon, J.F.; Hopfensitz, A.; De Neys, W. The modular nature of trustworthiness detection. Journal of Experimental Psychology: General 2013, 142, 143–150. [Google Scholar] [CrossRef]
  9. Bromhall, C. (2003). The Eternal Child: An Explosive New Theory of Human Origins and behaviour. Ebury, London. 34-36, 196, 281-283.
  10. Brown, D.G.; Cotton, C.A. A comment on the statistical analysis in Kasumovic and Kuznekoff 2015 PLoS ONE.
  11. Brown, D.G.; Cotton, C.A. (2015b). Further comment on Kasumovic and Kuznekoff (2015).
  12. Cantu, E.; Jussim, L. (2021). Microaggressions, questionable science, and free speech. Texas Review of Law & Politics.
  13. Carpenter, S.J. Implicit gender attitudes. Dissertation Abstracts International: Section B: The Sciences and Engineering, 61(10-B), 5619.
  14. Chamberlain, J.; Holroyd, J.; Jenkins, B.; Scaife, R. Implicit bias, intersectionality, compositionality. Philosophical Psychology 2023, 1–27. [Google Scholar]
  15. Connor, P.; Weeks, M.; Glaser, J.; Chen, S.; Keltner, D. Intersectional implicit bias: Evidence for asymmetrically compounding bias and the predominance of target gender. Journal of Personality and Social Psychology 2023, 124, 22–48. [Google Scholar] [CrossRef] [PubMed]
  16. Crown Prosecution Service (2024). Data Summary Quarterly Update. Quarter 2 of 2023-2024.Table 3.3.
  17. Cummins, D.D. Dominance hierarchies and the evolution of human reasoning. Minds & Machines 1996, 6, 463–480. [Google Scholar] [CrossRef]
  18. Cummins, D.D. Evidence for the innateness of deontic reasoning. Mind & Language 1996, 11, 160–190. [Google Scholar] [CrossRef]
  19. Cummins, D.D. Evidence of deontic reasoning in 3- and 4-year-olds. Memory & Cognition 1996, 24, 823–829. [Google Scholar] [CrossRef]
  20. Cummins, D.D. Cheater detection is modified by social rank. Evolution and Human Behavior 1999, 20, 229–248. [Google Scholar] [CrossRef]
  21. Cummins, D.D. (2005). Dominance, status, and social hierarchies. In Buss, D.M. (ed) The Handbook of Evolutionary Psychology, 676-697. Wiley.
  22. Cummins, D.D. Deontic Reasoning as a Target of Selection: Reply to Astington and Dack(2013). Journal of Experimental Child Psychology 2013, 116, 970–974. [Google Scholar] [CrossRef]
  23. Cummins, D.D. (2019). Dominance Theory (Cummins). In: Shackelford, T. & Weekes-Shackelford, V. (eds) Encyclopedia of Evolutionary Psychological Science. Springer.
  24. David-Barrett, T.; Rotkirch, A.; Carney, J.; Behncke Izquierdo, I.; Krems, J.A.; Townley, D.; McDaniell, E.; Byrne-Smith, A.; Dunbar, R.I.M. Women favour dyadic relationships, but men prefer clubs: Cross-cultural evidence from social networking. PLoS ONE 2015, 10. [Google Scholar] [CrossRef]
  25. Dupanloup, I.; Pereira, L.; Bertorelle, G.; Calafell, F.; Prata, M.J.; Amorim, A.; Barbujani, G. A recent shift from polygyny to monogamy in humans is suggested by the analysis of worldwide Y-chromosome diversity. Journal of Molecular Evolution 2003, 57, 85–97. [Google Scholar] [CrossRef]
  26. Eagly, A.H.; Mladinic, A.; Otto, S. Are women evaluated more favorably than men? An analysis of attitudes, beliefs and emotions. Psychology of Women Quarterly 1991, 15, 203–216. [Google Scholar]
  27. Eagly, A.H.; Mladinic, A. Gender stereotypes and attitudes toward women and men. Personality and Social Psychology Bulletin 1989, 15, 543–558. [Google Scholar] [CrossRef]
  28. Esperanza, R.A.M. (2020). The Slow Progression of Women into Executive-Level Positions within the State of Hawaii, Island of Oahu: A Qualitative Phenomenological Study (Doctoral dissertation, Northcentral University).
  29. Favre, M.; Sornette, D. Strong gender differences in reproductive success variance, and the times to the most recent common ancestors. Journal of Theoretical Biology 2012, 310, 43–54. [Google Scholar] [CrossRef] [PubMed]
  30. Fiddick, L.; Cummins, D.D. Reciprocity in ranked relationships: Does social structure influence social reasoning? Journal of Bioeconomics 2001, 3, 149–170. [Google Scholar] [CrossRef]
  31. Galinsky, A.D.; Hall, E.V.; Cuddy, A.J.C. Gendered races: Implications for interracial marriage, leadership selection, and athletic participation. Psychological Science 2013, 24, 498–506. [Google Scholar] [CrossRef] [PubMed]
  32. Garcia Coll, C.; Lamberty, G.; Jenkins, R.; McAdoo, H.P.; Crnic, K.; Wasik, B.H.; et al. An integrative model for the study of developmental competencies in minority children. Child Development 1996, 67, 1891–1914. [Google Scholar] [CrossRef]
  33. Goetz, C.D.; Easton, J.A.; Lewis, D.M.; Buss, D.M. Sexual exploitability: Observable cues and their link to sexual attraction. Evolution and Human Behavior 2012, 33, 417–426. [Google Scholar] [CrossRef]
  34. Goff, P.A.; Thomas, M.A.; Jackson, M.C. “Ain’t I a woman?” Towards an inter-sectional approach to person perception and group-based harms. Sex Roles 2008, 59, 392–403. [Google Scholar] [CrossRef]
  35. Goodwin, M.H. Exclusion in girls’ peer groups: Ethnographic analysis of language practices on the playground. Human Development 2002, 45, 392–415. [Google Scholar] [CrossRef]
  36. Gwinn, O.S.; Brooks, K.R. Race-contingent face after-effects: A result of perceived racial typicality, not categorization. Journal of Vision 2013, 13. [Google Scholar]
  37. Haddock, G.; Zanna, M.P. Preferring housewives to feminists: Categorization and the favorability of attitudes toward women. Psychology of Women Quarterly 1994, 18, 25–52. [Google Scholar] [CrossRef]
  38. Hales, D.; Edmonds, B. Intragenerational cultural evolution and ethnocentrism. Journal of Conflict Resolution 2019, 63, 1283–1309. [Google Scholar] [CrossRef]
  39. Harris, P.L.; Nuñez, M. Understanding of permission rules by preschool children. Child Development 1996, 67, 1572–1591. [Google Scholar] [CrossRef]
  40. Hook, N. (2019). May the force of gender be with you: Identity, identification and ’own-gender bias’. Describing a new experimental method and new findings. 165-182 in: Barry, J., Kingerlee,R., Seager, M. & Sullivan, L. (eds) The Palgrave Handbook of Male Psychology and Mental Health. Palgrave Macmillan.
  41. Ishii, K.; Numazaki, M. Effect of salience of the gender category on men’s automatic gender prejudice under self-threat. Japanese Journal of Social Psychology 2015, 31, 25–34. [Google Scholar]
  42. Johnson, K.L.; Freeman, J.B.; Pauker, K. Race is gendered: How covarying phenotypes and stereotypes bias sex categorization. Journal of Personality and Social Psychology 2012, 102, 16–131. [Google Scholar] [CrossRef]
  43. Jones, C.R.; Fazio, R.H. Person categorization and automatic racial stereotyping effects on weapon identification. Personality and Social Psychology Bulletin 2010, 36, 1073–1085. [Google Scholar] [CrossRef]
  44. Kasumovic, M.M.; Kuznekoff, J.H. Insights into sexism: Male status and performance moderates female-directed hostile and amicable behaviour. PLoS ONE 2015, 10, e0138399. [Google Scholar] [CrossRef]
  45. Kaukinen, C. The help-seeking decisions of violent crime victims: An examination of the direct and conditional effects of gender and the victim-offender relationship. Journal of Interpersonal Violence 2002, 17, 432–456. [Google Scholar] [CrossRef]
  46. Koda, T.; Tsuji, S.; Takase, M. (2022). Measuring subconscious gender biases against male and female virtual agents in Japan. In Proceedings of the 10th International Conference on Human-Agent Interaction 275-277.
  47. Krylov, A.I.; Tanzman, J. Critical social justice subverts scientific publishing. European Review 2023, 31, 527–546. [Google Scholar] [CrossRef]
  48. Laniado, D.; Volkovich, Y.; Kappler, K.; Kaltenbrunner, A. Gender homophily in online dyadic and triadic relationships. EPJ Data Science 2016, 5, 19. [Google Scholar] [CrossRef]
  49. Lilienfeld, S.O. Microaggressions: Strong claims, inadequate evidence. Perspectives on Psychological Science 2017, 12, 138–169. [Google Scholar] [CrossRef]
  50. Lilienfeld, S.O. Microaggression research and application: Clarifications, corrections, and common ground. Perspectives on Psychological Science 2020, 15, 27–37. [Google Scholar] [CrossRef]
  51. Lindenlaub, I.; Prummer, A. (2013). More Versus Closer Friends: How Gender Shapes Social Networks and their Effects on Performance. 18th CTN Workshop, Dept of Economics, University of Warwick. http://www2.warwick.ac.uk/fac/soc/economics/events/seminarsschedule/conferences/ctn/prummer_paper.pdf.
  52. Liu, T.; Wong, Y.J. The intersection of race and gender: Asian American men’s experience of discrimination. Psychology of Men & Masculinity 2018, 19, 89–101. [Google Scholar] [CrossRef]
  53. Lui, P.P.; Berkley, S.R.; Pham, S.; Sanders, L. Is microaggression an oxymoron? A mixed methods study on attitudes toward racial microaggressions among United States university students. PLoS ONE 2020, 15. [Google Scholar] [CrossRef] [PubMed]
  54. Maddux, W.; Brewer, M. Gender differences in the relational and collective bases for trust. Group Processes Intergroup Relations 2005, 8, 159–171. [Google Scholar] [CrossRef]
  55. Mehl, B.; Buchner, A. No enhanced memory for faces of cheaters. Evolution & Human Behavior 2008, 29, 35–41. [Google Scholar] [CrossRef]
  56. Misch, A.; Dunham, Y.; Paulus, M. The developmental trajectories of racial and gender intergroup bias in 5-to 10-year-old children: The impact of general psychological tendencies, contextual factors, and individual propensities. Acta Psychologica 2022, 229, 103709. [Google Scholar] [CrossRef]
  57. Möller-Leimkühler, A.M. Barriers to help-seeking by men: A review of sociocultural and clinical literature with particular reference to depression. Journal of Affective Disorders 2002, 71, 1–9. [Google Scholar] [CrossRef]
  58. Montagu, A. (1989). Growing Young (2nd ed.). Granby, M.A.: Bergin & Garvey.
  59. Morin, R. (2015). Exploring Racial Bias Among Biracial and Single-Race Adults: The IAT, Pew Research Center’s Social & Demographic Trends Project. United States of America. Retrieved from https://coilink.org/20.500.12592/rxz10n on 01 Feb 2025.
  60. Moxon, S.P. The sexes serve to purge mutations by selection on males, boosting the function of sex to maintain genomic integrity. New Male Studies 2019, 8, 25–51. [Google Scholar]
  61. Moxon, S.P. (2021). Male heterogeneity and female choice in human mating: Maximising female fertility by offsetting stress, age and unwanted attention while facilitating extra-pair conception. New Male Studies 10.1, 8-29 (part 1) & 10.2 (part 2).
  62. Moxon, S.P. Dominance hierarchy is male-specific: It is not bi-(inter-)sexual nor female. New Male Studies 2023, 12, 1–28. [Google Scholar]
  63. Nadal KLY (2023) A review of microaggression literature In, K.L.Y. Nadal, Dismantling everyday discrimination: Microaggressions toward LGBTQ people (2nd ed., pp. 41–53). American Psychological Association.
  64. Nosek, B.A.; Banaji, M.R. The go/no-go association task. Social Cognition 2001, 19, 625–666. [Google Scholar] [CrossRef]
  65. O’Donohue, W.T. (2023). Prejudice and the quality of the science of contemporary social justice efforts in psychology. Pp 173-200 in Frisby, C.L., Redding, R.E., O’Donohue, W.T. & Lilienfeld, S.O. (eds) Ideological and Political Bias in Psychology: Nature, Scope, and Solutions. Cham: Springer International Publishing.
  66. Oda, R. Biased face recognition in the prisoner’s dilemma game. Evolution & Human Behavior 1997, 18, 309–315. [Google Scholar] [CrossRef]
  67. Perszyk, D.R.; Lei, R.F.; Bodenhausen, G.V.; Richeson, J.A.; Waxman, S.R. Bias at the intersection of race and gender: Evidence from preschool-aged children. Developmental Science 2019, 22. [Google Scholar] [CrossRef]
  68. Petsko, C.D.; Bodenhausen, G.V. Multifarious person perception: How social perceivers manage the complexity of intersectional targets. Social and Personality Psychology Compass 2019. [Google Scholar] [CrossRef]
  69. Pignolet, Y.A.; Schmid, S.; Seelisch, A. Gender-specific homophily on Instagram and implications on information spread. Scientific Reports 2024, 14, 451. [Google Scholar] [CrossRef]
  70. Rainville, R.E.; Gallagher, J.G. Vulnerability and heterosexual attraction. Sex Roles 1990, 23, 25–31. [Google Scholar] [CrossRef]
  71. Rasmussen, M.L.; Hjelmeland, H.; Dieserud, G. Barriers toward help-seeking among young men prior to suicide. Death Studies 2018, 42, 96–103. [Google Scholar] [CrossRef]
  72. Richeson, J.A.; Ambady, N. Who’s in charge? Effects of situational roles on automatic gender bias. Sex Roles 2001, 44, 493–512. [Google Scholar] [CrossRef]
  73. Rudman, L.A.; Goodwin, S.A. Gender differences in automatic in-group bias: Why do women like women more than men like men? Journal of Personality and Social Psychology 2004, 87, 494–509. [Google Scholar] [CrossRef]
  74. Seaton, E.K.; Caldwell, C.H.; Sellers, R.M.; Jackson, J.S. The prevalence of perceived discrimination among African American and Caribbean black youth. Developmental Psychology 2008, 44, 1288–1297. [Google Scholar] [CrossRef]
  75. Sellers, R.M.; Shelton, J.N. The role of racial identity in perceived racial discrimination. Journal of Personality & Social Psychology 2003, 84, 1079–1092. [Google Scholar] [CrossRef]
  76. Sivan, J.; Curry, O.S.; Van Lissa, C.J. Excavating the foundations: Cognitive adaptations for multiple moral domains. Evolutionary Psychological Science 2018, 4, 408–419. [Google Scholar] [CrossRef]
  77. Skowronski, J.J.; Lawrence, M.A. A comparative study of the implicit and explicit gender attitudes of children and college students. Psychology of Women Quarterly 2001, 25, 155–165. [Google Scholar] [CrossRef]
  78. Steele, J.R.; Lipman, C. Children’s implicit attitudes toward targets who differ by both race and gender. Developmental Psychology 2023, 59. [Google Scholar] [CrossRef]
  79. Syed, M. The logic of microaggressions assumes a racist society. Perspectives on Psychological Science 2021, 16, 926–931. [Google Scholar] [CrossRef] [PubMed]
  80. Szell, M.; Thurner, S. How women organize social networks different from men. Scientific Reports 2013, 3, 1214. [Google Scholar] [CrossRef]
  81. Tung, F.W. (2011) Influence of gender and age on the attitudes of children towards humanoid robots. In: Jacko J.A. (eds) (2011). Human-computer interaction. Users and applications. HCI2011. Lecture Notes in Computer Science, vol 6764. Springer, Berlin, Heidelberg.
  82. Veenstra, G. The gendered nature of discriminatory experiences by race, class, and sexuality: A comparison of intersectionality theory and the subordinate male target hypothesis. Sex Roles 2013, 68, 646–659. [Google Scholar] [CrossRef]
  83. van Lier, J.; Revlin, R.; de Neys, W. Detecting cheaters without thinking: Testing the automaticity of the cheater detection module. PLoS ONE 2013, 8, e53827. [Google Scholar] [CrossRef]
  84. Vogel, D.L.; Heath PJ (2016) Men masculinities help-seeking patterns In, Y.J. Wong & S.R. Wester (Eds.) APA Handbooks in Psychology Series. APA Handbook of Men and Masculinities (pp. 685–707). American Psychological Association. Washington DC, USA.
  85. Walters, M.A.; Krasodomski-Jones, A. (2018). Patterns of Hate Crime: Who, What, When and Where? Table 20, p39. University of Sussex and Demos.
  86. West-Eberhard, M.J. The maintenance of sex as a developmental trap due to sexual selection. Quarterly Review of Biology 2005, 80, 47–53. [Google Scholar] [CrossRef]
  87. Wilder, J.A.; Mobasher, Z.; Hammer, M.F. Genetic evidence for unequal effective population sizes of human males and females. Molecular Biology & Evolution 2004, 21, 2047–2057. [Google Scholar] [CrossRef]
  88. Yamaguchi, M.; Beattie, G. The role of explicit categorization in the Implicit Association Test. Journal of Experimental Psychology: General 2020, 149, 809–827. [Google Scholar] [CrossRef]
  89. Yang, X.; Yang, F.; Guo, C.; Dunham, Y. Which group matters more: The relative strength of minimal vs. gender and race group memberships in children’s intergroup thinking. Acta Psychologica 2022, 229, 103685. [Google Scholar] [CrossRef]
  90. Yousaf, O.; Grunfeld, E.A.; Hunter, M.S. A systematic review of the factors associated with delays in medical and psychological help-seeking among men. Health Psychology Review 2015, 9, 264–276. [Google Scholar] [CrossRef]
  91. Ziv, T. (2012). An Examination of the Own-Race Preference in Infancy. Doctoral dissertation, Harvard University.
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