Race and IQ: A Theory-Based Review of the Research in Richard Nisbett - s Intelligence and How to Get It
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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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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 | 0.023 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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