Legacy and sustainability in the Olympic Movement’s <i>new norm</i> era: when reforms are not enough
Bibliographic record
Abstract
In times when cities around the world are withdrawing from consideration to host the Olympic Games, the International Olympic Committee (IOC) has responded by enacting policy reforms. Entitled Olympic Agenda 2020: 20 + 20 Recommendations, we examine forty recommendations through the lens of critical policy analysis. Specifically, we use Strittmatter et al.’s framework of legitimation strategies in policy formulation and implementation to investigate the ways in which the use of sustainability and legacy terminology is employed in the process of legitimating the IOC today. Findings demonstrate two general legitimation strategies. One is the familiar framing of the Olympic Games in terms of sustainability and legacy with a new emphasis on the Games as an opportunity to integrate cities’ long-term planning needs into bidding and hosting the event. The second focus asserts the Olympic Movement’s global leadership role in sport in the context of sport itself being depicted as a leading social institution in making meaningful social change. In this paper, we detail these legitimation strategies and offer commentary related to the need for the IOC to use this policy reform process to move beyond rhetoric alone to embed meaningful and measurable accountability standards in the hosting process.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".