Bibliographic record
Abstract
This brief epilogue highlights the ways in which the Winter Olympics are a unique case for the examination of protest in and through sport, with events that are almost entirely Western and Northern and facility requirements that limit their geographic potential. Concerns for the preservation of the natural world, as well as opposition to development, inflected some of the earliest anti-Winter Olympic protests. These objections were advanced primarily by white, middle-class protestors who had the ability to influence public narratives regarding development and public expenditures. Other voices, including those of Indigenous Peoples, were largely absent from such debates. As the Winter Games have increasingly found themselves hosted in Asia, opposition to the development of the natural world has continued to struggle to influence coalitions of developers and civic boosters. The impacts of such global events are felt at the local level, both by residents whose lives are impacted in often negative ways and by the protestors who invest much of their identities in what are increasingly difficult battles. There is evidence of the success of such struggles, especially as they inform transnational coalitions, through the number of high-profile winter-sport cities (e.g., Calgary, Oslo) where residents are rejecting public expenditures on winter Olympic bids.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.460 | 0.192 |
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".