Spearman’s hypothesis tested comparing Korean young adults with various other groups of young adults on the items of the Advanced Progressive Matrices
Bibliographic record
Abstract
Spearman's hypothesis tested at the subtest level of an IQ battery states that differences between races on the subtests of an IQ battery are a function of the g loadings of these subtests, such that there are small differences between races on subtests with low g loadings and large differences between races on subtests with high g loadings. Jensen (1998) stated that Spearman's hypothesis is a law-like phenomenon. It has also been confirmed many times at the level of items of the Raven's Progressive Matrices. This study hypothesizes that with concern to Spearman's hypothesis, subtests and items function in fundamentally the same way, and tested whether Spearman's hypothesis is confirmed at the item level for White-East Asian comparisons. A group of Korean young adults (N=205) was compared with other groups of young adults from Canada, the US, Russia, Peru and South Africa (total N=4770) who took the Advanced Progressive Matrices. Spearman's hypothesis was strongly confirmed with a sample-size-weighted r with a value of 0.63. Computing the g loadings of the items of the Raven with either the Raven-g or the Wechsler-g led to the same conclusions. Tests of Spearman's hypothesis yielded less-strong outcomes when the 36-item Advanced Progressive Matrices were used than when the 60-item Standard Progressive Matrices were used. There is a substantial correlation between sample size and the outcome of Spearman's hypothesis. So, all four hypotheses were confirmed, showing that a part of the subtest-level nomological net replicates at the item level, strengthening the position that, with concern to Spearman's hypothesis, subtests and items function fundamentally the same. It is concluded that Spearman's hypothesis is still a law-like phenomenon. Detailed suggestions for follow-up research are made.
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".