UNRAVELING ETHNIC DENSITY EFFECTS, ACCULTURATION, AND ADJUSTMENT: THE CASE OF RUSSIAN-SPEAKING IMMIGRANTS FROM THE FORMER SOVIET UNION
Bibliographic record
Abstract
There has been limited advancement in the empirical literature unpacking the ethnic density effect, a social ecological phenomenon that may help explain some of the conflicting findings in bidimensional acculturation research. In this study, we developed a brief measure of perceived ethnic density in a community sample of Russian-speaking immigrants (N = 269) in Montreal, Canada, finding it to be a superior predictor of distress to objective linguistic density. Acquiring social support partly mediated the relation between perceived ethnic density and lower distress. Furthermore, the relation between heritage acculturation and distress was double moderated by perceived ethnic density and time lived in the neighborhood. A person–ecology match involving heritage acculturation and ethnic density was related to better psychological adjustment for participants who had resided in their neighborhood for less than, but not more than, 2 years. Clinical and community research implications for using measures of perceived ethnic density and acculturation measurement are discussed.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".