General or Situational? Exploring Cultural Identification Patterns Using Entropy Among Maghrebi Immigrants to Canada
Bibliographic record
Abstract
Migrants and members of cultural minorities must negotiate their identification with multiple cultural groups. Many studies have investigated associations between general questionnaire–based cultural identity patterns and psychological adjustment. Research on situational cultural identity patterns—context-bound, momentary identification with a given cultural group—is scarcer. Furthermore, we know little about how variability in identification across contexts and situations may be associated with psychological adjustment. This study addresses these issues by (a) comparing the relative ability of general questionnaire–based and situational diary–based cultural identity patterns in statistically predicting psychological adjustment among Maghrebi migrants to Canada, and (b) introducing and testing cultural identity entropy, a novel approach to characterizing variability in a person’s multiple cultural identities during daily interactions. Drawing on concepts in thermodynamics and information theory, cultural identity entropy indexes greater balance in one’s multiple identifications during an interaction and reflects greater flexibility in cultural ways in that moment. Participants were 93 Maghrebi migrants to Canada who completed baseline questionnaires and daily diaries on situational identification during interactions for 7 days. Results show that situational diary–based cultural identity patterns accounted for substantial variance in psychological adjustment, above and beyond general questionnaire–based patterns, and that greater entropy in heritage cultural contexts was associated with greater psychological adjustment. These results underscore the importance of going beyond general characterizations of multicultural identity by investigating the shifting and contextual ways in which migrants mobilize and negotiate their cultural identities in daily life.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".