Between the Strategic Summit and the Operational Centre: An Organizational Analysis of the “Choisir de Gagner” Sport Project for Youth with Disabilities
Bibliographic record
Abstract
Background.Choisir de Gagner is a Quebec (Canada) company associated to AlterGo and Défi sportif.Its mission is to promote a healthy lifestyle for youth with disabilities.Objectives.This article analyzes this organization, especially its strengths and weaknesses of its environments.Methods.The study took place over a three-year period, from 2012-2015.The first two data collections (2012-2013 and 2013-2014) aimed at analyzing the organization's internal components.In reference to these results, the third collection's goal (2015) was to analyze the transfer of knowledge that was developed during the organization's mandate.Results.Moreover, following the first data collection, a few weaknesses were identified regarding time management and the decision-making process.With regard to these results, certain adjustments were made in the organization which have led to a considerable improvement if we rely on the results from the second data collection.At the end of the article, the organization's external communication is specifically examined by an analysis of the transfer of knowledge.Several tools have been elaborated by the organization between 2012 and 2015.Conclusion.The main issues with respect to these tools are the respect and follow-up of each stage of the transfer process in order to ensure its sustainability.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".