A Theoretical Framework of Communal Resistance to Mega-Events
Bibliographic record
Abstract
Activists resist events in diverse ways to address many social problems. We synthesize 20 years of academic literature and data on how and why activists have opposed the bidding, staging, and legacy fallout of the Summer and Winter Olympic Games, providing a comprehensive overview of Olympic resistance. Evidence is presented from a transnational resistance movement perspective and through case-by-case analysis of international events, including historical cases (Beijing 2008; Vancouver 2010; London 2012; Sochi 2014; Rio 2016; PyeongChang 2018; Tokyo 2020; Beijing 2022) and current cases (Paris 2024; LA 2028). Findings reveal a typology of resistance approaches. We explain their importance for each case, detailing key stakeholders, their roles in resistance, where it occurs, and when it emerges. Based on this analysis, we present a theoretical framework of communal resistance to large-scale events, generalizable to contested major sporting and cultural contexts. We conclude with managerial recommendations and a future research agenda, focused on exploring resistance beyond Olympic contexts, effectiveness of resistance tactics, and how transnational networks form, operate, and influence policy and planning in an increasingly digitized world.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".