Understanding subgroup differences with general mental ability tests in employment selection: Exploring socio‐cultural factors across inter‐generational groups
Bibliographic record
Abstract
Abstract In employment selection, general mental ability (GMA) tests predict training and job performance but also lead to subgroup differences which in turn can produce adverse impact against minority groups. Although researchers have explored genetic, developmental, and environmental explanations for ethnic group differences, few studies have explored socio‐cultural factors comparing immigrant and non‐immigrant job applicants. Given that many ethnic job applicants may also be immigrants, understanding these factors can provide insight into GMA test score differences. The purpose of this paper is to explore the impact of individual and socio‐cultural factors on GMA test scores with immigrant and non‐immigrant bus driver applicants. This is the first study of its kind to our knowledge that has attempted to disentangle the effects of socio‐cultural factors from race/ethnicity in the study of subgroup differences. Incorporating these variables between non‐visible minority and minority groups accounted for considerable variance in GMA test scores across groups. The implications of focusing on socio‐cultural variables to enhance our understanding of subgroup differences are discussed. Our results specifically suggest that practitioners attend to the issue of the intersecting grounds of potential discrimination when using GMA tests in personnel selection.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".