Mapping the Landscape of Organized Sport in a Community: Implications for Community Development
Bibliographic record
Abstract
This study presents the landscape of private community sport organizations in the City of London, Ontario, Canada based on a profile of organizational features that align conceptually with critical aspects of community development. Features representing the scope-variety of sports offered, program age targets, and other offerings-and operations-nonprofit/commercial sector, open/closed program type, independent/affiliated/franchise status, and shared/exclusive facility use-of community sport organizations were captured from publicly available information about the population of 218 organizations. The location of sport delivery points for each organization was also mapped. The landscape is characterized by a balance of nonprofit and commercial organizations, offering a wide variety of sports, across all ages and City districts, but predominantly offered through closed programming that typically requires an extended financial commitment. Community sport organizations in this city are also most likely to operate independently, and share facilities. These features, and the landscape, are conceptualized as having implications for access, social inclusion, engagement and citizenship, and social capital that are fundamental to community development. Mapping the landscape in this community provides a valuable resource for understanding that potential.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".