Marching in the Glory: Experiences and Meanings When Working for a Sport Mega-Event
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".