Exploring young peoples’ experiences within a sport and livelihood program
Bibliographic record
Abstract
Unemployment rates among young people in Canada have consistently been double those of adults (Statistics Canada, 2016), currently ranging from 9-12% (Statistics Canada, 2023). Labour policies have drawn upon progressive youth development models to address this concern (Bancroft, 2017), with various initiatives emerging following the COVID-19 global pandemic. Sport and livelihood programming (SLP) uses sport programming to support career and economic development within under-resourced communities. The purpose of this study was to explore youths’ experiences within a SLP, with a secondary focus of exploring constructions of class, race, gender, poverty, (dis)ability and culture within programming. Seven participants (ages 19-25) enrolled in a fellowship program at a large sport for development centre engaged in semi-structured interviews. Drawing upon thematic analysis, four aggregate themes emerged from the data. Participants described program experiences as offering customized learning experiences and opportunities to develop corporate skills, despite some challenges related to program structure. Participants appreciated the SLP community, as they felt cared for, developing connections, while appreciating the prestige of their employer/organization. Nonetheless, participants expressed concerns about workplace culture and infrastructure, discussing issues related to race, whiteness, codeswitching, and equity, diversity, and inclusion. In considering the program’s contribution to their livelihoods, they expressed a desire for additional support in attaining full time employment, to in turn earn a living wage. Analyses suggest SLP may (re)produce some structural barriers of precarious working conditions. Future research is needed to explore programming from an interdisciplinary and/or transdisciplinary (Whitley et al, 2022) perspective.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".