Investigating how boxing interventions may support youth in northern British Columbia: A qualitative study
Bibliographic record
Abstract
Sport-based interventions (SBIs) are one method proven to help at-risk youth improve their lives by building relationships with positive adult and peer role models and providing a physical goal oriented activity with which to engage. This study examined boxing as a SBI intervention, looking specifically at if and how it may improve the lives of at-risk youth in Prince George and 100 Mile House, BC. SBIs have been shown to empower youth to choose differently by assisting them to develop positively (Wright, 2006; Pollack, 1998). My research focused on the sport of boxing as an intervention to assist youth towards more positive development, especially for at-risk youth living in northern British Columbia (BC), to form positive relationships, gain empowerment to make healthy choices, and decrease violent behaviours. To collect data, open-ended, semi-structured interviews were conducted. There were eight interviews completed with two boxing coaches, two adult boxers, who have been boxing since their youth, and four youth boxers ranging in age from 15 to 17 years old. Thematic analysis was conducted with the interview transcripts, which yielded three main findings: influence of boxing, boxing can teach life skills, and coaches’ have a positive influence. The significance of this research is well-timed and important. In a northern BC community with fewer resources available as compared to urban geographies, an SBI might be a more viable option to help youth become connected to their communities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".