Race and IQ: A Theory-Based Review of the Research in Richard Nisbett - s Intelligence and How to Get It
Bibliographic record
Abstract
We provide a detailed review of data from psychology, genetics, and neuroscience in a point-counterpoint format to enable readers to identify the merits and demerits of each side of the debate over whether the culture-only (0% genetic- 100% environmental) or nature + nurture model (50% genetic-50% environmental) best explains mean ethnic group differences in intelligence test scores: Jewish (mean IQ = 113), East Asian (106), White (100), Hispanic (90), South Asian (87), African American (85), and sub-Saharan African (70). We juxtapose Richard Nisbett s position, expressed in his book Intelligence and How to Get It , with our own, to examine his thesis that cultural factors alone are sufficient to explain the differences and that the nature + nurture model we have presented over the last 40 years is unnecessary. We review the evidence in 14 topics of contention: (1) data to be explained; (2) malleability of IQ test scores; (3) cultureloaded versus g-loaded tests; (4) stereotype threat, caste, and “X” factors; (5) reaction-time measures; (6) within-race heritability; (7) between-race heritability; (8) sub-Saharan African IQ scores; (9) race differences in brain size; (10) sex differences in brain size; (11) trans-racial adoption studies; (12) racial admixture studies; (13) regression to the mean effects; and (14) human origins research and life-history traits. We conclude that the preponderance of evidence demonstrates that in intelligence, brain size, and other life history traits, East Asians average higher than do Europeans who average higher do South Asians, African Americans, or sub-Saharan Africans. The group differences are between 50 and 80% heritable.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".