Second language acquisition and acculturation: similarities and differences between immigrants and refugees
Bibliographic record
Abstract
Refugees and immigrants differ in their reasons for migration and their criteria for entry into Canada. While economic immigrants migrate to other countries voluntarily, refugees are forced to leave their countries due to fear of death or persecution. Due to the difference in the nature of resettlement, assumptions exist that immigrants and refugees may differ in terms of emotional well-being, social adjustment and acculturation, and second language learning outcomes. To assess these assumptions, this study was conducted on a sample (N = 45) of newcomer Iranian immigrants (Mage = 19.24, SD = 2.06) and refugees (Mage = 23.15, SD = 4.02). The participants completed a series of questionnaires regarding their English language and literacy skills, acculturation, socioeconomic status, emotional well-being, and potential traumatic experiences in the past. This study examined the relationships among these variables for the two groups. The refugees scored lower on variables related to socioeconomic status and had lower English skills than the immigrant group. English word reading and vocabulary were related to second language reading comprehension for immigrants, but only word reading was related to reading comprehension for refugees. The experienced trauma was significantly higher among the refugees. However, the trauma was not a significant predictor for any of the English proficiency skills. Acculturation was related to English reading comprehension, and enculturation was negatively associated with English vocabulary and reading comprehension for refugees but not for immigrants. The findings point to similarities and differences between refugees and immigrants. Recommendations to facilitate resettlement 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".