London 2012 Olympics and the Power of the British Trade Unions: A Golden Opportunity?
Bibliographic record
Abstract
Since their modern inception in 1896, the Olympics have grown in size and stature to become one of the most important mega-sport events. However, unlike other mega-sport events the Olympics has its own value-laden philosophy of “Olympism”, advocating sport as a vehicle for social change. This paper utilises Eric Batstone’s (1988) three-fold power schema of disruptive potential, labour scarcity and political influence to explore the impact of London 2012 on the power of the British unions. To achieve this, it draws on a comparative study of the National Union of Rail, Maritime and Transport Workers (RMT) and the Musicians’ Union (MU). Based on findings generated from interviews and secondary-data analysis this paper will argue that the collective bargaining results of unions in the run-up to and during the 2012 Olympic Games were a reflection of the individual unions’ pre-existing power – those that had more disruptive, labour scarcity or political power prior to the Games were able to win more benefits for their members, whereas those with less were either less successful or did not succeed at all in their negotiations. In addition, when evaluating the power sources, an “Olympic factor” can be observed, which produces a differentiated impact on the power resources of the unions.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".