Confronting scientific racism in psychology: Lessons from evolutionary biology and genetics.
Bibliographic record
Abstract
Although the American Psychological Association has taken a strong antiracism stance, scientific racism continues to be published in psychology journals and scholarly books. Recent articles claim that the folk categories of race are genetically meaningful divisions and that evolved genetic differences among races and nations are important for explaining immutable differences in cognitive ability, educational attainment, crime, sexual behavior, and wealth; all claims that are opposed by a strong scientific consensus to the contrary. These claims remain a serious source of harm through the naturalization of inequality and through support for the work of racial extremists. Contemporary "racial hereditarian research" claims to rest on modern genetics and evolutionary biology and to draw on their methods, such as genome-wide association studies. These new arguments fail to meet the evidentiary and ethical standards of these disciplines for the study of human variation. If psychology adopted standards from genetics and evolutionary biology, the current racial hereditarian work would be ineligible for publication. Actions that the American Psychological Association can take to deal with scientific racism are described. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".