Case for Non‐Biased Intelligence Testing Against Black Africans Has Not Been Made: A Comment on Rushton, Skuy, and Bons (2004)
Bibliographic record
Abstract
This reply reviews the conceptual, methodological, and statistical foundations of Rushton, Skuy and Bons' article in this journal that compared Black Africans, Whites and East Indians on the Raven's Advanced Progressive Matrices, and concluded that the Raven's is an unbiased test. Through a technical re‐analysis of both the internal and external validity criteria for test bias using data reported in the Rushton et al. paper, we demonstrate that the Raven's Matrices test is in fact biased against Black Africans. We take issue with several additional elements of Rushton et al.'s study, including the use of non‐equivalent groups in test samples. We briefly review Rushton's racial‐realist research agenda and show that the assumption of test bias is central to advancing that agenda. Industrial/organizational and occupational psychologists should critically analyze and re‐evaluate the science employed in Rushton's racial‐realist research and also should better understand the ethical and social implications of accepting his reports of research findings on test bias and White–Black IQ differences as established scientific facts.
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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 | 0.034 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.073 | 0.070 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".