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Record W4207074061 · doi:10.1080/1369183x.2022.2029372

Foreclosed futures and entangled timelines: conceptualization of the ‘future’ among Syrian newcomer mothers in Canada

2022· article· en· W4207074061 on OpenAlexafffundabout
Laila Omar

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2022
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsTimelineForced migrationRefugeeFutures contractScholarshipConceptualizationFeelingSociologyFraming (construction)Gender studiesSocial psychologyPsychologyGeographyPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.332
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2022
Admission routes3
Has abstractyes

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