Bibliographic record
Abstract
Idealist rhetoric has served the interests of all players in the Olympic industry for more than a century, and continues to do so, despite global resistance campaigns and indisputable evidence of the International Olympic Committee’s self-serving hypocrisy. Much of this rhetoric has its origins in nineteenth-century Europe, as expressed by Coubertin, the founder of the modern games. In Western democracies, propaganda that invokes Olympic ideals and values floods the media, the school system, and even the academy. Many liberal critics have relied on idealist arguments, apparently in an attempt to appeal to Olympic leaders’ and sponsors’ sense of decency and commitment to the moral principles embedded in the Olympic Charter. However, reliance on idealism has become so exploited and tainted by Olympic industry leaders that its use is both unwise and ineffective. History has shown that these principles have repeatedly been ignored by the IOC, national Olympic committees, international sports federations, bid and organizing committees, and athletes. A more productive approach to Olympic protest calls for politicians and business leaders in bid and host cities to assess all the economic, social, and environmental costs associated with hosting the games.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.074 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".