Leveraging Events to Develop Collaborative Partnerships: Examining the Formation and Collaborative Dynamics of the Ontario Parasport Legacy Group
Bibliographic record
Abstract
The strategic formation of partnerships for leveraging sport events to achieve social impact is becoming a critical component of large-scale sport events. The authors know less about the process dimensions related to the formation and collaborative dynamics of a sport event–leveraging partnership. To address this gap, the authors focus on examining the formation and collaborative dynamics alongside the challenges of the cross-sector partnership, the Ontario Parasport Legacy Group (OPLG), which emerged as an important leveraging strategy for the Toronto 2015 Pan/Parapan American Games. The authors found that the formation of the OPLG was shaped through broader environmental elements—including resource conditions, window of collaborative opportunity, and cultural influence—and essential drivers of strategic leadership and consequential incentives. Furthermore, the authors’ analysis shows that the development of the OPLG and its effectiveness in partnership delivery were determined through key domains of collaborative dynamics (i.e., engagement, motivation, and joint capacity).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".