Hope and Strength(s) Through Physical Activity for Canada’s Aboriginal Peoples
Bibliographic record
Abstract
Abstract Purpose – In this chapter, I explore and argue for a theoretical shift in research about Aboriginal physical activity practices in Canada, from a deficit perspective to a strengths perspective that incorporates practices of hope. Design/methodology/approach – After briefly describing my concerns about analysing Aboriginal physical activity practices from a deficit perspective, I outline, apply and argue for the benefits of a research approach that begins with a strengths perspective and incorporates practices of hope. Findings – I argue that all individuals have strengths and places where they can exercise power. An adoption of complementary power relations framed within the practices of hope, which include availability and listening with an openness to co-transformation, further clarifies how to adopt a strengths perspective analysis of Aboriginal physical activity practices. Originality/value – In adopting a strengths perspective, I am committed to actively identifying existing strengths as a starting point, along with resources that can be used to further those strengths. Strengths are then used to address identified barriers to physical activity. The practices of hope outline how non-Aboriginal allies can work alongside Aboriginal individuals to co-transform physical activity in a manner that enhances physical activity practices for all those involved.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".