Foreclosed futures and entangled timelines: conceptualization of the ‘future’ among Syrian newcomer mothers in Canada
Bibliographic record
Abstract
Scholars have focused significant attention on geographic aspects of forced migration, as well as the economic, psychological, and cognitive outcomes of refugees’ movement across space. Less attention has been paid to temporal aspects of forced migration, particularly after refugees’ resettlement in a host society. Using semi-structured interviews with 41 Syrian mothers who recently arrived in Canada, I contribute to building theory on temporal dimensions of forced migration through an analysis of how refugee mothers conceptualise both their children’s and their own futures. First, I show how mothers’ perceptions of the future are heavily shaped by cultural and religious orientations of divine control, which may be out of sync with norms in their host society. I also identify two patterns demonstrating how space and time intersect. Displaced mothers deliberately ‘foreclose’ their own timeline in order to focus on their children’s future in Canada, feeling like their ‘selves’ could only grow in the former geographic space. Moreover, mothers do not separate their future projections from the present in Canada or from the past in Syria, leading to ‘entangled timelines.’ These findings suggest that scholarship on forced migration may continue to benefit from attention to how time and temporal experiences shape outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.020 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".