Bibliographic record
Abstract
The study of the political and economic aspects of the Olympic Games has increased in the recent years (see Girginov 2010; Lenskyj and Wagg 2012; Giulianotti et al. 2015). These new and markedly critical Olympic studies have contributed significantly to our understanding of the Games (and mega sporting events generally) and their impact on society—particularly on the host city and nation. The study of anti-Olympic campaigns holds a key role in this wider academic research. One of the main reasons that academics/researchers study the anti-Olympic groups and movements around the world is indirectly to investigate the IOC and the Olympic Games themselves. The bulk of the ‘anti-Olympic’ research projects that have been carried out in this area (notably by the Canadian academics and activists Helen Lenskyj and Christopher Shaw—see Lenskyj 2000, 2002, 2008; Shaw 2008) have opened up the discussion about previously neglected aspects of the Games and have shed light on their impact on a huge swathe of the host society. Yet, these findings have rarely been appreciated, or even acknowledged, by the IOC and the local Olympic Games organising committees (LOCOGs). The growing opposition towards the Olympics and, lately, the diminished interest shown by cities in hosting the Games (both summer and winter) demonstrate that the same problems and controversies reappear, and expand to include wider areas of economic and social life. It is evident, after several decades of research into the Games, that the constitution of the IOC, the organisational framework of the Olympics and the strategic planning of the hosting cities/nations, routine promises and assessments notwithstanding, do not result in beneficial social, environmental or economic impacts. On the contrary, as we may conclude from previous research, they may contribute to the widening of economic inequality, facilitate corruption and, thus and bearing in mind the scandals engulfing world football’s governing body FIFA in 2015 (Jennings 2015), bring elite sport further into disrepute. 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".