Politicians’ Personal Legacies from Olympic Bids and Referenda—An Analysis of Individual Risks and Opportunities
Bibliographic record
Abstract
The popularity of staging Olympic Games has dropped in democratic countries as a series of failed referenda and withdrawn bids as well as protests against mega sport events have shown in recent years. Nevertheless, the there still are democratically elected office-holders willing to become involved in an Olympic bid despite the high probability of public opposition and the threat of an almost unwinnable referendum. This conceptual study analyses the individual risk management that these politicians have to concern themselves with because of their involvement in Olympic bids and referenda. It does so by looking at possible ‘personal legacies’ the politicians can obtain. It is interesting to note that although the size of such legacies will vary, they can result irrespective of the outcome of a bid or a referendum and can have positive, negative, or neutral effects for the politician(s) in question. As will be shown, personal legacies can also be obtained by opponents of Olympic bidding ambitions, which is not the only finding that is problematic particularly for the IOC and National Olympic Committees interested in hosting Olympic Games or other sport events.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".