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Record W4414087140 · doi:10.1080/14927713.2025.2555568

In the shadow of multiculturalism: leisure, gender, and race in the refugee experience in Canada

2025· article· en· W4414087140 on OpenAlexafffundvenueabout
Monir Shahzeidi, Moss E. Norman

Bibliographic record

VenueLeisure/Loisir · 2025
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRace (biology)RefugeeShadow (psychology)ImmigrationEthnic group

Abstract

fetched live from OpenAlex

This critical commentary examines the leisure experiences of racialized girls and women refugees in Canada through an intersectional lens, foregrounding the complex socio-political and cultural power relations that shape their access to, and engagement in, leisure. The article offers an evaluative analysis of existing scholarship to deepen understandings of how leisure is constrained and shaped by intersecting systems of oppression. In foregrounding intersectional analysis, this commentary underscores that leisure for racialized girls and women refugees cannot be understood or advanced within assimilationist or neoliberal models. Instead, it requires a sustained intersectional analysis of how existing immigration, resettlement, and leisure policies reinforce systems of exclusion, while also recognizing the resilience and agency of racialized girls and women refugees in negotiating these barriers. By advocating for intercultural and justice-oriented leisure frameworks, this paper calls for structural transformation that recenters the voices, needs, and cultural knowledges of racialized refugees in Canada.

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)0.001
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 score0.120
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.035
GPT teacher head0.328
Teacher spread0.292 · 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

Citations1
Published2025
Admission routes4
Has abstractyes

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