Callie Burt

This manuscript offers a rejoinder to Ogunbajo et al.’s study of “gender affirmation and adoption interest” among transgender populations and to their published reply to my critique. I argue that the study’s findings are unreliable because the study’s analytic strategy is not supported by its data structure. Specifically, the fitted multinomial model is applied to covariate patterns that are too sparse and imbalanced to sustain the adjusted comparisons the authors report, much less the substantive and policy claims they derive from them; key variables are operationalized in ways that conflate gender affirmation with socioeconomic advantage and misrepresent central constructs; and the article’s descriptive and reporting errors further undermine confidence in its findings. At a conceptual level, I question the treatment of “adoption interest” as a public health outcome and unqualified normative good rather than as a family-formation preference whose health relevance must be argued or measured. I conclude by using this case to reflect on how epistemic norms and methodological standards are enforced—or relaxed—in contested policy domains.

Scholars have long warned that ideological homogeneity in social science can compromise the error-correcting mechanisms of scientific inquiry, but how this process operates in practice, and how fragile claims become stabilized as settled knowledge, remains insufficiently understood. In this article, I examine how field-level ideological commitments embed contestable assumptions upstream of empirical testing, shaping what can be asked, measured, and concluded. I conduct an Ideological Epistemic Analysis (IEA) of a widely cited and highly publicized study treated as definitive evidence that gender-identity-prioritizing public-accommodations policies pose no public-safety risk. I show that a priori ideological commitments, operating through category reconstitution—especially the displacement of sex as an analytically relevant category—structure the study’s framing, constrain which mechanisms can be examined, and shape interpretation before data are examined. I further document how a methodologically flawed design produced findings that were nonetheless converted into settled science through canonization bias, a process by which ideologically congenial but fragile evidence is elevated to authoritative status through citation, media uptake, and policy discourse. Rather than reflecting a curious anomaly or isolated failure, the case illuminates a broader epistemic pattern in which ideological homogeneity weakens organized skepticism and facilitates the canonization of fragile claims. I also suggest avenues for mitigating ideological epistemic distortions and promoting more robust social science.

Scholars have long warned that ideological homogeneity in social science may produce theoretical and epistemic distortions. However, whether such distortions occur (and, if so, where and how) remains uncertain. In this work, I discuss the threat of field-level ideological capture to the validity of science and highlight the need for systematic efforts to identify ideologically captured fields and assess epistemic biases. I begin by outlining how ideological homogeneity may shape research practices and distort the broader knowledge-production process. Drawing on scholarship on ideological epistemology, I demonstrate how shared assumptions and normative commitments may systematically bias the collection, evaluation, and interpretation of evidence. To facilitate critical engagement with these biases, I introduce Ideological Epistemic Analysis (IEA) as an approach for identifying and evaluating ideological influences within research fields. I then conduct an IEA case study of transgender research. The analysis reveals how gender-identity ideology can permeate this literature, shaping research framing, methods, and interpretations. I argue that these ideological commitments compromise scientific rigor, with both scientific and social consequences, including the erosion of public trust in science and the development of misguided or ineffective policies that may ultimately harm the very communities the research aims to support. Although I focus on transgender research as a case study, I emphasize the broader need for heightened epistemic vigilance in the social sciences, particularly in fields characterized by ideological homogeneity and addressing politically sensitive topics.

Uchiyama et al. propose a unified model linking cultural evolutionary theory to behavior genetics (BG) to enhance generalizability, enrich explanation, and predict how social factors shape heritability estimates. A consideration of culture evolution is beneficial but insufficient for purpose. I submit that their proposed model is underdeveloped and their emphasis on heritability estimates misguided. I discuss their ambiguous conception of culture, neglect of social structure, and the lack of a general theory in BG.

The sociogenomics revolution is upon us, we are told. Whether revolutionary or not, sociogenomics is poised to flourish given the ease of incorporating polygenic scores (or PGSs) as “genetic propensities” for complex traits into social science research. Pointing to evidence of ubiquitous heritability and the accessibility of genetic data, scholars have argued that social scientists not only have an opportunity but a duty to add PGSs to social science research. Social science research that ignores genetics is, some proponents argue, at best partial and likely scientifically flawed, misleading, and wasteful. Here, I challenge arguments about the value of genetics for social science and with it the claimed necessity of incorporating PGSs into social science models as measures of genetic influences. In so doing, I discuss the impracticability of distinguishing genetic influences from environmental influences because of non-causal gene–environment correlations, especially population stratification, familial confounding, and downward causation. I explain how environmental effects masquerade as genetic influences in PGSs, which undermines their raison d’être as measures of genetic propensity, especially for complex socially contingent behaviors that are the subject of sociogenomics. Additionally, I draw attention to the partial, unknown biology, while highlighting the persistence of an implicit, unavoidable reductionist genes versus environments approach. Leaving sociopolitical and ethical concerns aside, I argue that the potential scientific rewards of adding PGSs to social science are few and greatly overstated and the scientific costs, which include obscuring structural disadvantages and cultural influences, outweigh these meager benefits for most social science applications.