Running Head: ASSESSING SPORT WITH INDIGENOUS FRAMEWORK
Bibliographic record
Abstract
The importance of sport and recreation is recognized worldwide reflected in policy, such as the United Nations Convention on the Rights of the Child (United Nations High Commissioner for Human Rights, 1990). Largely due to competition for limited resources, subsidized sports programs in lower income communities have to demonstrate evidence of their success. This has led to increased research exploring the impacts of sport, particularly related to social and personal development. The outcomes of success have focused on improving problematic behaviours such as criminal activity or the development of strengths within ‘at-risk’ communities (Coakley, 2002). With both approaches there still remains a lack of understanding of what is it about sport that impacts social and personal development (Canadian Parks and Recreation Association, 1994; Coakley, 2002; Halas, 2001; Hartmann, 2003). Researchers suggest that a new approach is needed. Responding to the need for a new approach, this research conceptualized the topic through an indigenous research framework and employed two indigenous methods, sharing circles and piloting of a new technique, Anishnaabe Symbol-Based Reflection (ASBR). The impact of a martial art program for participants at an urban Aboriginal cultural centre were explored using these two methods. Some of the salient themes that emerged are discussed in this paper. The Canadian sport system will benefit from this research with increase in knowledge regarding the personal and social impacts of sport while providing a different lens and methods from which to explore this topic.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".