Cultural identity development in adult forced migrants: The psychometrics of a measure in Arabic, Spanish, and Ukrainian
Bibliographic record
Abstract
The aim of the present study was to assess the psychometric equivalence of Arabic-, Spanish-, and Ukrainian-language versions of the Multigroup Ethnic and National Identity Measure (MENI), a measure of cultural identity development comprised of an ethic identity and a national identity scale. The psychometric properties of the three language versions were examined and confirmatory factor analysis (CFA) was conducted to evaluate model fit and test measurement invariance in a sample of adult forced migrants from Syria, Mexico, and Ukraine living in Canada ( N = 616). Multigroup CFA provided support for scalar invariance of the ethnic identity scale, allowing meaningful comparisons across the three cultural/language groups. However, the national identity scale demonstrated only configural invariance, suggesting that, although the general structure was consistent, the strength and patterns of relationships differed across groups. The construct and criterion validity of both scales were adequate for assessing identification with the country of origin and the residence country across the three language groups. Based on these findings, we conclude that future research can use the Arabic-, Spanish-, and Ukrainian-language versions of the MENI to assess and compare cultural identity development across these cultural/language groups of adult (forced) migrants, derive identity statuses, and extract acculturation profiles.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".