Bibliographic record
Abstract
The last decade of the 20th century experienced a resurgence of genetically based theories of racial hierarchy regarding intelligence and morality. Most notably was Herrnstein and Murray's The Bell Curve (1994), that claimed genetic causality for long-standing racial differences in IQ. In addition, it raised the time worn argument that the over-reproduction of genetically deficient individuals within our population would lead to a serious decline in average American intelligence. These authors provided no specific rationale for why these genetic differences should exist between human `races'. Instead, they relied heavily on the work of Canadian psychologist J. Philipe Rushton (in The Bell Curve, 1994, Appendix 5: 642—3). Rushton has advanced a specific evolutionary genetic rationale for how gene frequencies are differentiated between the `races' relative to intelligence. He claims that human racial differences result from natural selection for particular reproductive strategies in the various racial groups. Rushton's theory is based entirely on the concept of r- and K-selection, first explicitly outlined by MacArthur and Wilson in 1967. This article examines both the flaws in the general theory, and specifically Rushton's application of that same theory to human data. It concludes that neither Rushton's use of the theory nor the data that he has assembled could possibly test any meaningful hypotheses concerning human evolution and/or the distribution of genetic variation relating to reproductive strategies or `intelligence', however defined.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.022 | 0.049 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.018 |
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".