Collaborations between National Olympic Committees and public authorities
Bibliographic record
Abstract
1. Purpose\nThe primary purpose of the project was to empirically map collaborations between National Olympic Committees (NOCs) and public authorities in diverse regional and cultural context. \n\n2.Objectives\nRegarding practical implications, the project aimed to identify best practice examples for suc-cessful collaborations between NOCs and public authorities and to provide empirically sup-ported guidance on how to structure collaborative relationships between the NOCs and public authorities. In terms of academic relevance, the project intended to expand the knowledge about key institutions in global sport governanceby examining the autonomy, control and governance contributions of NOCs. The project aimed to make the contributions of NOCs to domestic sport policies evident as well as different ways how NOCs pursue the goals of the Olympic Move-ment. Moreover, the project inquired how and to what extent NOCs are in control of these collaborations. Hence, the academic objective of the project was to contribute to a more general theory on sport governance, which pays attention to political and culture diversity. \n\n3.Methodology\nThe projectemployed an analytical case study approach. More precisely, we conducted com-parative analytical case studies on collaborations between NOCs and public authorities by ap-plying a policy perspective. The fine-grained investigation of policy specific collaborations re-lied on the content analysis of policy documents, collaboration agreement, project records etc. as well as on a more than fifty expert interviews. Each case study included interviews from three NOC members of staff, governmental authorities, externals. The first part of the interview guide was dedicated to basic structural features of NOC-government collaborations. The second part of the guide addressed the management of specific collaborations. Data analysis followed the iterative procedure as out(Miles & Huberman, 1994). The coding of the material was done both inductively and deductively (Braun and Clarke2006:83; see also Braun, Clarke & Weate,2016).
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".