Forced migration, resettlement, and sport: Lessons from the Kabul-Edmonton soccer team
Bibliographic record
Abstract
Forced migration is one of the most pressing crises of our lifetime. Of the millions forced to migrate, many come to know the brutality of state-managed migration that habitually denies asylum seekers and places substantive restrictions on refugees who have been resettled. Sociologists of sport and leisure have examined the sporting experiences of refugees through an intersectional lens, foregrounding how displacement and resettlement are differently lived and negotiated across overlapping power structures and markers of gender, sexuality, ethnicity, religion, and legal status. Through a participatory and collective photovoice project, this article explores the experiences of an all-Afghan soccer team that played in a social, co-ed soccer league in the spring of 2022, just after they arrived in Edmonton, Alberta, Canada. In photovoice narratives and subsequent interviews, team members underlined many of the barriers they faced as they navigated the formal and informal rules and dominant norms of this seemingly inclusive sports landscape. In doing so, they revealed some of the limits of official discourses of Canadian multiculturism, which rarely accommodate more significant forms of difference, and which reproduce racial and ethnic hierarchies that powerfully discipline newcomers who are encouraged to embrace their precarious status as model minorities.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".