The Ontario Parasport Legacy Group: A Case Study of a Collaborative Partnership as an Event Legacy
Bibliographic record
Abstract
The strategic formation of collaborative partnerships for leveraging sport events to achieve social impacts is becoming a critical component of large-scale sport events, evidenced in London 2012 Olympics and 2014 Commonwealth Games in Glasgow, among others. There have been relatively few studies that focus on improving our understanding of how collaborative governance influences the formation and collaborative dynamics of such cross-sector collaborations. The purpose of this study is to contribute research in this area by examining the collaborative governance components encompassing the formation and collaborative dynamics of the cross-sectoral partnership, the Ontario Parasport Legacy Group, which emerged as part of the leveraging strategy for the Toronto 2015 Parapan American Games. This study draws upon the Integrative Framework for Collaborative Governance proposed by Emerson, Nabatchi, and Balogh (2012) to examine the factors influencing the formation and collaborative dynamics of cross-sector collaborations. The study findings are presented through an analysis of resources, drivers, participant fit, principled engagement, shared motivation, and informality of management processes. Practical and theoretical implications as well as directions for future research are provided.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".