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Record W2261707419 · doi:10.15173/glj.v7i1.2786

London 2012 Olympics and the Power of the British Trade Unions: A Golden Opportunity?

2016· article· en· W2261707419 on OpenAlexvenueno aff
Helen Russell

Bibliographic record

VenueGlobal Labour Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationScarcityPower (physics)PoliticsBargaining powerSoft powerCollective bargainingPolitical scienceSociologyEconomyPolitical economyEconomicsLawMarket economy

Abstract

fetched live from OpenAlex

<p style="margin: 0cm 0cm 0pt; text-align: justify; line-height: 150%;"><span style="line-height: 150%; font-family: 'Times New Roman','serif'; font-size: 12pt;">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.</span></p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.273
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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