“Criminals” and “Killjoys”: An Exploratory News Media Mapping of the Environment-Security Politics at the Olympics
Bibliographic record
Abstract
Over the past 4 decades, the International Olympic Committee (IOC) has increasingly foregrounded environmental issues in the staging of Olympic Games and selection of host cities. At the same time, critiques of the IOC’s environmental turn point to the unsustainable legacy of the Games and demonstrate the failure of host cities to meet their environmental goals. Research has also revealed the rapid expansion of a surveillance and security infrastructure surrounding the Olympics. Such developments demonstrate how the environmental and security arms of the IOC, seemingly disparate, may share a synergistic relationship that serves the interest of the IOC and its partners and that attempts to silence environmental activists. Thus, the objective of this paper was to identify and map news media coverage of this relationship over the modern Olympic Games. To address this objective, we asked the following research questions: What connection exists within news media coverage of environmental approaches and Olympic-based security and surveillance? And what does this coverage reveal about the relationship between the IOC, its partners, and environmental activists? Our mapping of these trends begins with the Sydney 2000 Summer Games and ends with the lead-up to the Paris 2024 Games. Our analysis of the news media corpus illuminates the connections between the IOC’s approach to the environment and the interests of its public and private partners, including host cities and nations, corporate sponsors, and construction firms. This mutually beneficial alignment is protected through the development of an extensive security and surveillance apparatus around the Olympics that suppresses activists and others advocating for alternative forms of environmentalism. The IOC’s oppressive tactics are enabled, in part, by the dominant characterization of environmental activists as criminals, killjoys, and antithetical to Olympism.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".