Who Are You—Right Now? Cultural Orientations and Language Used as Antecedents of Situational Cultural Identification
Bibliographic record
Abstract
A defining feature of biculturalism is the experience of switching back and forth between different cultural ways of being and acting in the world. This work investigates antecedents of this switching process using a cultural adaptation of the Day Reconstruction Method, in which participants divide the previous day into episodes and then rate these episodes on various criteria. We hypothesized that episode characteristics (specifically, language used) and stable personal dispositions (specifically, mainstream and heritage cultural orientations) would independently and interactively predict migrants’ cultural identification during an episode. We examined three types of identification among Russian-speaking migrants to Canada ( N = 109): mainstream (“Canadian”); heritage (“Russian”); and mainstream–heritage hybrid (“Russian-Canadian”). Results of multilevel regression analyses supported our hypotheses overall. A more positive orientation to a given cultural group and the use of that group’s language(s) were associated with stronger identification with that group during an episode. Language Use × Cultural Orientation interactions were evident for heritage and hybrid situational identification. The positive association between heritage orientation and situational heritage identification was stronger during episodes when the heritage language was not used than when it was used. A positive heritage orientation was associated with greater situational hybrid identification only during episodes when a mainstream language was used. The results are consistent with the perspective that acculturation is a multifaceted, contextual, and dynamic process whereby people acquire and flexibly use multiple cultural repertoires to meet both their general goals and the cultural demands of specific situations.
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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 | 0.007 |
| 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 | 0.001 | 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".