“Nothing Is Gonna Change If We Don't Care for Everyone”: A Narrative Inquiry Alongside Urban Indigenous Youth in an Afterschool Physical Activity Wellness Program
Bibliographic record
Abstract
In the fall of 2013, the authors received funding to help develop and implement an afterschool wellness program alongside Indigenous youth aged 6-10 years old in the North Central neighborhood of Regina, Saskatchewan, Canada. The Growing Young Movers (GYM) afterschool program was funded, in part, as a corrective response to a broader social trend in which Indigenous youth in this neighborhood reported declining health and wellness outcomes, as well as multiple other barriers to social inclusion. This article discusses the reflections of three senior high school Indigenous youth (16-18 years old) who participated in the afterschool program as peer-mentors over a 2-year period from 2015 to 2017. Our inquiry reveals how these youth viewed the program-and their role(s) within it-in far more complex, active, and even political terms, than the program's initial framing as a physical activity-based "intervention" had anticipated. Our analysis (re)positions youth according to their own personalized voice and narratives as: cultural leaders, knowledge holders, and as agents of change in their community.
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 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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".