Bibliographic record
Abstract
Rio has a lot to win from the Games … And the Olympic movement has a lot to win from Rio as well. 1 According to Tomlinson, ‘the allegedly pure Olympic ideal has always been moulded into the image of the time and place of the particular Olympiad or Games’. 2 The contextuality of the Olympic Games, to which Tomlinson referred, is particularly evident in the way that virtually every modern games has been immersed within, and simultaneously an agent of, the domestic and international politics of the moment. Despite masquerading behind a veneer of political neutrality - originally advanced by Coubertin et al. as a cornerstone of the Olympic movement - the politically motivated actions of the national organising committees, and at times the events which enveloped succeeding Olympic Games, have rendered apoliticism little more than an anachronistic part of the Olympics’ brand identity. 3 While discussions of the politicisation of the contemporary Olympics routinely default to the monumentally politicised Olympic spectacles - such as Berlin 1936, Moscow 1980, Salt Lake City 2002 and Beijing 2008, to name but a few - it is our contention that analysis of less overtly politicised games is equally instructive. It is this assumption that drew us to the phenomenon of Rio 2016. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".