Basic values as a motivational framework relating individual values with acculturation strategies among Arab immigrants and refugees across different settlement contexts
Bibliographic record
Abstract
There is a lack of systematic acculturation research on the motivations underpinning the behavior of migrants, which could explain how they acculturate and adapt to their new country of residence. This paper examines the link between values, using the Schwartz Theory of Basic Human Values, and acculturation strategies among Arab immigrant and refugee groups across different settlement contexts. The results of Study 1 (Arab immigrants; N = 456) showed, as hypothesized, positive links between strategies and values: the integration strategy with conservation, social focus, self-protection, and self-transcendence values; assimilation with openness to change, personal focus, and growth values; and separation with conservation, social focus, and self-protection. These findings were generally repeated in Study 2 (Syrian refugees; N = 415) except that integration was not associated with self-transcendence and that assimilation was positively linked to self-enhancement instead of openness to change. Our analyses indicated that acculturation preferences are mainly related to motivational values, rather than to different settlement contexts in both samples; however, assimilation seems to be more associated to context than values among the refugee sample. Implications of the findings to the acculturation literature 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 | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 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".