Diversity in Sports and Recreation: A Challenge or an Asset for the Municipalities of Greater Montréal?
Bibliographic record
Abstract
Today, ethnocultural diversity is a fact of life in big cities, and indeed in smaller ones, where it sometimes serves as a means of promotion. How are cities responding to the issues raised by the growing numbers of people who have di! erent needs and di! erent tastes? is article o! ers some answers based on the fi ndings of two surveys of practices followed in managing diversity, conducted in Greater Montreal. Municipalities are developing a variety of responses to diversity: some are adopting policies that advocate accommodation, others favour a universal approach. In the fi eld of recreation, various issues arise: infrastructures (redesign of recreational spaces) and interethnic cohabitation (changes in preferences, group issues). Generally, Montreal’s municipalities are responding ad hoc, case by case, in a pragmatic spirit, as seen in the case of pool management. e management of diversity may thus seem to be improvised, but this approach has the advantage of allowing gradual adaptation to the di! erences among residents, with a view to fostering reciprocal learning.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".