Spearman's Law of Diminishing Returns in Normative Samples for the WISC-IV and WAIS-III
Bibliographic record
Abstract
In order to explain observed variations in intelligence test scores, Spearman (1927 ) proposed the “law of diminishing returns” (SLODR). It states that the g saturation of cognitive ability tests decreases as a function of ability or age. Published studies have shown mixed results. However, a recent review ( Hartmann & Nyborg, 2004 ) suggests that there is evidence for differences in g saturation by ability level, but that observed age effects on g saturation are most likely to be a consequence of the ability effect. The current study analyzed the standardization data of the most recent Wechsler scales for both children and adults from several different countries. This study did not find evidence to support either the ability or age version of SLODR by using large normative samples for the WISC-IV from the United States, Canada, and Australia, and for the WAIS-III from the same three countries and also from The Netherlands.
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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 | 0.038 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".