Second Language Acquisition and Acculturation Differences Between Immigrants and Refugees
Bibliographic record
Abstract
The main difference between refugees and immigrants lies in the reason for their migration. Economic immigrants migrate to other countries voluntarily, while refugees are forced to leave their countries due to fear of death or persecution (UNHCR, 2018). Such fears may lead to psychological trauma among refugees. Research has shown that the presence of trauma can negatively impact language learning (Iversen, Sveaass, & Morken, 2014), which may have important implications for both second language (L2) acquisition and acculturation, particularly in refugees. In addition, strong linguistic abilities in a first language (L1) may be beneficial to acquire other languages (Cook, 2003). This study examined the relationships between L1 skills, L2 fluency, acculturation and socio-emotional variables among newcomer Iranian refugees and immigrants and second-generation immigrants in Canada. The results indicated that the experience of trauma in refugees was significantly higher compared to the other two groups in the study. Moreover, there was a significant difference among the three groups in terms of English fluency skills. Refugees had the lowest English fluency skills, the immigrant group performed significantly better than refugees, but lower than second-generation counterparts. The English word reading efficiency was the only variable related to English reading comprehension for the Iranian refugees. However, both English word reading and English vocabulary were significantly related to L2 reading comprehension for Iranian immigrants. There were no differences between Iranian refugees and immigrants’ acculturation or enculturation. For refugees, mainstream acculturation was a significant predictor of English reading comprehension, and heritage enculturation was a significant predictor of English vocabulary and English reading comprehension. However, acculturation or enculturation were not related to English reading comprehension or heritage enculturation for Iranian immigrants. L1 fluency and other socio-emotional variables did not predict L2 fluency for any of the groups. These findings highlight important issues, first the importance of examining acculturation and enculturation on L2 acquisition. In addition, findings identify the importance of considering the differences between refugees and immigrants concerning second language acquisition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".