International Olympic Resistance: Thinking Globally, Acting Locally
Bibliographic record
Abstract
In keeping with the theme of this conference, The Global Nexus Engaged, this paper will examine Olympic resistance, paying particular attention to two related premises: 1. that resistance to the Olympic industry is global; and 2. that anti-globalization and anti-Olympic protesters are working together to target corporate sponsors and orga-nizers of the Olympic Games. Olympic resistance Despite the suppression of political dissent required by Rule 61 of the Olympic Charter and the host city contract, and despite the necessary illusion of unqualified local support on the part of bid and host cities, various forms of Olympic resistance can be found in every recent bid city, as well as numerous past, present, and future host cities (with the possible exception of Beijing, where suppression of dissent appears to be the norm). With the terms of debate firmly established by Olympic boosters and their public relations consultants in the early days of the bid process, community-based critics have often found themselves in a "David vs. Goliath " predicament in terms of media expo-sure and public response. Despite the obstacles, grassroots organizations, such as the Toronto Bread Not Circuses Coalition, which opposed Toronto's bids for the 1996 and the 2008 Olympics, and the Berlin group, which opposed that city's 2000 bid, were able to focus some public debate on the potentially negative economic and social impact issues and played an important role in the defeat
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.046 |
| Scholarly communication | 0.040 | 0.029 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".