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Record W2159610401 · doi:10.1123/jsm.23.2.210

Marching in the Glory: Experiences and Meanings When Working for a Sport Mega-Event

2009· article· en· W2159610401 on OpenAlexaff
Xiaoyan Xing, Laurence Chalip

Bibliographic record

VenueJournal of Sport Management · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsLaurentian University
Fundersnot available
KeywordsBeijingContext (archaeology)Event (particle physics)SociologyPrivilege (computing)BureaucracyGloryPublic relationsChinaPoliticsPolitical scienceHistoryLaw

Abstract

fetched live from OpenAlex

Sport mega-event organizing committees have three uniquely challenging characteristics: They grow rapidly; they are temporary; they are accountable for event symbolisms. Effects of these characteristics are examined via participant observation and in-depth interviews with twelve lower-level employees of the Beijing Organizing Committee for the Olympic Games (BOCOG) two years before the Beijing Olympics. Four themes about their working lives were identified: The daily work is mundane; BOCOG is bureaucratic; privilege has its privileges; my immediate working environment nurtures me. The mega-event context was also important; workers described it using: The Olympics are great and grand; the Olympics are valuable for China; the Olympics illustrate the challenges that China faces in the 21st century; BOCOG is uniquely high profile; BOCOG helps us to understand Chinese society. Employees used four themes to describe the coping strategies they applied to manage the challenges of working for the organizing committee: I have to confront or adjust; my work at BOCOG allows me to develop myself; working at BOCOG represents a passionate life with idealism; I get to be part of history. Findings suggest that social support, the symbolic significance of the event, and learning through event work mitigate the stresses of working to host a mega-event. Future work should examine the workers’ lives longitudinally over the lifespan of an organizing committee to delineate the dynamics of meanings and experiences in mega-event work.

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)0.004
metaresearch head score (Gemma)0.000
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 score0.821
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.033
GPT teacher head0.320
Teacher spread0.287 · 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

Citations47
Published2009
Admission routes1
Has abstractyes

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