‘If I Knew How to Speak English…’: How language shapes refugee mothers’ perceptions of past, present, and future in Canada
Bibliographic record
Abstract
Abstract What role does language learning play in refugees’ memory-construction on the one hand, and imagining of the future self on the other? Using a temporal perspective on migration, I extend scholarship examining the role of language in the space-time continuum of resettlement. With three waves of semi-structured interviews with twenty Syrian refugee mothers (N = 60) who have recently arrived in Canada, this article examines how their experiences with time and future projections are influenced by their experiences of language learning in the host country. First, mothers’ lack of English proficiency and struggle to learn leads to a sense of nostalgia towards the past, where their proficiency in Arabic is associated with past feelings of comfort, security, and mastery. In addition, mothers find themselves ‘stuck’ in the present, where multiple structural barriers (e.g., absence of extended kin; limited government support) and individual challenges (e.g., health issues; having children with disability) significantly slow down their language acquisition process and prevent them from achieving other goals. This leads to a clear conflict between government expectations for the long-term future and the mothers’ immediate priorities. Finally, despite those government temporal expectations building on newcomers’ language acquisition, mothers do not want to envision the future due to past experiences of uncertainty, belief in divine control, and a foreclosure of the future. This article demonstrates the ways in which language, space, and time co-construct notions of the future, and a sense of potential ‘stuckness,’ well beyond the temporal limits of intensive state intervention in refugee lives.
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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 | 0.003 | 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 | 0.015 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".