The actualities of doing community development to promote the inclusion of low income populations in local sport and recreation
Bibliographic record
Abstract
While there is literature to suggest that community development is a promising strategy for involving marginalized citizens in local sport and recreation, little research has been done on the actualities of doing this type of work from the perspectives of those engaged in it. Based on our observations and interviews over three years with members of a community‐based project designed to increase the Involvement of women living below the poverty line in local sport and recreation, we identified six key dimensions of community development. These dimensions were highly inter‐related and included: a shared concern about a social problem requiring action, encouraging the active participation of a marginalized group, forming public sector partnerships to pool resources and build political support, adopting collaborative principles of organizing, collectively developing and implementing action plans, and re‐conceptualizing traditional notions of accountability. In this paper, we analyze each dimension by discussing the benefits and challenges encountered from the perspectives of the women on low income and the public sector partners involved in the project. We conclude that while community development is a complex and challenging strategy, it shows considerable promise for including those who are least likely to be involved in the planning and participation of local sport and recreation programs thereby increasing local governments’ ability to meet their mandates of providing access for all citizens.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 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".