The (mis)measure of misbehavior: Cross-national invariance of the Youth Externalizing Problems Screener across 32 countries
Bibliographic record
Abstract
The present study investigated the cross-national measurement invariance of a 10-item Youth Externalizing Problems Screener (YEPS) on a sample of 17,489 adolescents from 32 countries. The original one-factor and two-factor models of YEPS were found to provide a poor fit to the data in most countries. Following the removal of two semantically overlapping items and the inclusion of correlated error terms, adequate model fit was obtained in 31 of 32 countries. Measurement invariance testing of an abbreviated 8-item YEPS (YEPS-SF) supported configural invariance. Partial scalar invariance was achieved only after freely estimating numerous parameters. The alignment analysis revealed that 22% of parameters were non-invariant across countries. South Africa, Hungary, and India showed the largest number of non-invariant parameters, whereas the lowest number was detected in several European countries. These findings highlight the potential of the YEPS-SF for use within individual countries and the challenge of developing cross-culturally comparable measures, suggesting that cultural adaptations may be necessary.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".