International comparison of group differences in general mental ability for immigrants versus non‐immigrants
Bibliographic record
Abstract
Globalization has led to increased migration and labor mobility over the past several decades and immigrants generally seek jobs in their new countries. Tests of general mental ability (GMA) are common in personnel selection systems throughout the world. Unfortunately, GMA test scores often display differences between majority groups and ethnic subgroups that may represent a barrier to employment for immigrants. The purpose of this study was to examine differences in GMA based on immigrant status in 29 countries (or jurisdictions of countries) throughout the world using an existing database that employs high‐quality measurement and sampling methodologies with large sample sizes. The primary findings were that across countries, non‐immigrants ( n = 139,464) scored approximately half of a standard deviation ( d = .53) higher than first‐generation immigrants ( n = 22,162) but only one‐tenth of a standard deviation ( d = .12) higher than second‐generation immigrants ( n = 6,428). Considerable variability in effect sizes was found across countries as Nordic European and Germanic European countries evidenced the highest non‐immigrant/first‐generation immigrant mean differences and Anglo countries the smallest. Countries with the lowest income inequality tended to evidence the highest differences in GMA between non‐immigrants and first‐generation immigrants. Implications for GMA testing as a potential barrier to immigrant employment success and the field's current understanding of group differences in GMA test scores will be discussed.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".