Immigration, Integration, and Intersectoral Partnership: A Critical Examination of Introductory Winter Sport Programmes for Newcomers to Canada
Bibliographic record
Abstract
Sport participation is popularly constructed as a key aspect of integration for newcomers into their new countries of residence. These claims, however, are critiqued by sport and migration researchers. While newcomer sport participation is receiving increasing scholarly interest, lived experiences of newcomers in introductory winter sport programmes and the (in)efficacy of such programmes remains underexplored. Winter sports hold a central place within Canadian culture and identity, making the intersection between winter sport, immigration, integration, and Canadian identity a timely topic of investigation. I approached this research from a relativist ontological as well as interpretivist and social constructionist epistemological perspectives. The overarching purpose was to explore the realities of designing and implementing introductory winter sport programmes as well as the lived experiences of newcomer participants. First, six Canadian Intro to Sport programmes involving newcomers were examined using an interpretivist, qualitative research design. Results from this study were incorporated into designing the WinSport Newcomers Programme (WNP), an intersectoral community partnership comprising sport administrators, settlement service practitioners from three separate organizations, and me. I adopted a qualitative case study research design to explore the lived experiences of WNP participants and their parents as well as the (in)efficacy of the WNP as an intersectoral community partnership. Results illuminated the value of winter sport participation for newcomers to Canada as well as various challenges facing the sustainability of newcomer winter sport participation. The WNP represents a maturing intersectoral community partnership that has excelled at introducing newcomers to Canadian winter sports. Yet, various weaknesses (e.g., collaborators negotiating job-related time pressures) are obstructing the collaboration’s effectiveness. Together, the results critique and identify shortcomings in newcomer introductory winter sport programmes, while also offering important insights into how newcomer introductory winter sport programmes may be designed to foster positive 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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.056 | 0.027 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| 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".