No narrowing in mean Black–White IQ differences—Predicted by heritable g.
Bibliographic record
Abstract
Comments on the original article, "Intelligence: New findings and theoretical developments," by R. E. Nisbett, J. Aronson, C. Blair, W. Dickens, J. Flynn, D. F. Halpern, and E. Turkheimer (see record 2011-30298-001). The present authors assert Nisbett et al were incorrect when they claimed that between 1972 and 2002 there was a 5.5-point narrowing of the 15-point IQ gap between Blacks and Whites (p. 146). In doing so, they sidestepped Rushton and Jensen's (2006) objections to Dickens and Flynn's (2006) evidence and failed to include subsequent evidence. The present authors maintain that Nisbett et al failed to describe accurately how heritable g provides evidence of a significant genetic contribution to Black-White differences. The present authors claim Nisbett et al obscured the topic by invoking alleged age and social class interactions and adoption studies of very young children. Many twin and adoption studies have shown that by adolescence, there are equal heritabilities (about 50%) for Whites, Blacks, and East Asians (Hur, Shin, Jeong, & Han, 2006; Rushton & Jensen, 2010b). There is no evidence of any special cultural influence, such as extreme deprivation or being raised as a visible minority, that operates in one group and not in others.
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.
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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".