Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.383 | 0.014 |
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 it