Bibliographic record
Abstract
Abstract In this article, I draw on the philosopher of history Hayden White's typology of arguments to explain different accounts of bidding for the olympic games. White's typology helps explain the irreconcilable disconnect between representations of bidding for and hosting the olympic games put forward by the International Olympic Committee (IOC) and its academic supporters in olympic education, on the one hand, and their critics, on the other. While I advocate for contextual-based arguments as the most appropriate for understanding bidding at different points in the twentieth and twenty-first centuries, I conclude with an irony: the IOC's representations of bidding and hosting, which are based on organicist arguments presented in romantic and idealized narratives, continue to resonate better with a broad audience than fact-laden and eloquent contextualist arguments.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".