Comparing the Performance of Native North Americans and Predominantly White Military Recruits on Verbal and Nonverbal Measures of Cognitive Ability
Bibliographic record
Abstract
This study compared the cognitive ability of adult Canadian First Nations (American Indian) aboriginals (N=101) living in remote areas to recruits (N=131) undergoing military training in the Canadian Forces. Comparisons involved both verbal and nonverbal measures: The Canadian Forces Aptitude Test (CFAT), Wonderlic Personnel Test, (WPT), Raven's Standard Progressive Matrices (SPM) and Mill Hill Vocabulary (MHV) test. All measures were examined for differential test and item functioning; as well, the CFAT was analyzed for adverse impact. Confirming past research, First Nations (FNs) members scored, on average, 18 points lower in IQ estimates based on the WPT, a verbal measure of cognitive ability; however, the differences between the groups were less on the nonverbal tests: SPM (5 points lower) and the MHV (9 points lower). Differential Item Functioning (DIF) analysis detected a few items from the CFAT, SPM and MHV that displayed DIF, but none from the WPT. The SPM and WPT appear to be unbiased measures with respect to differential test functioning (DTF), once language and education are controlled. Making employment decisions solely on verbal cognitive ability test scores, however, is likely to produce adverse impact against members of FN, unless verbal ability is a bona fide occupational requirement for the positions under consideration. Both the verbal and nonverbal cognitive ability tests assessed the same latent structure suggesting that the nonverbal tests could be used in place of the verbal tests with little loss in predictive ability for occupations involving a high degree of spatial ability.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".