The Multicultural Identity Integration Scale (MULTIIS): Developing a comprehensive measure for configuring one’s multiple cultural identities within the self.
Bibliographic record
Abstract
OBJECTIVES: The research investigating how one's multiple cultural identities are configured within the self has yet to account for existing cultural identity configurations aside from integration, and for identifying with more than 2 cultural groups at once. The current research addresses these issues by constructing the Multicultural Identity Integration Scale (MULTIIS) to examine 3 different multicultural identity configurations, and their relationship to well-being based on Amiot and colleagues' (2007) cognitive-developmental model of social identity integration (CDSMII). METHOD: Diverse samples of multicultural individuals completed the MULTIIS along with identity and well-being measures. (Study 1A: N = 407; 1B: N = 310; 2A = 338 and 2A = 254) RESULTS: Reliability and confirmatory factorial analyses (Studies 1A and 2A) all supported the factorial structure of the MULTIIS. Regression analyses (Studies 1B and 2B) confirmed that the integration subscale of the MULTIIS positively predicted well-being, whereas compartmentalization negatively predicted well-being. Categorization was inconsistently related to well-being. CONCLUSIONS: These findings support the CDSMII and the usefulness of the MULTIIS measure, and suggest that each identity configuration is uniquely related to well-being outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".