Back to the Roots of Sports Management: 1980 Moscow and 1984 Los Angeles Organizing Committee of Olympic Games
Bibliographic record
Abstract
This research aims to compare the 1980 Moscow and 1984 Los Angeles Olympic Games (OG), which continued their activities in parallel with the ideologies of the two superpowers of the Cold War, the USA, and the USSR, with public and private sector sports management approaches. The research model is a comparative case study. The findings were reached by single and cross analysing the data sources, including OCOG's official reports. Moscow and Los Angeles OCOG activities were compared under organizational structure, facilities, financing, and expenditures headings. Despite the blurred lines of intersectoral distinction, Moscow OCOG (OCOG-80) stands out as a distinctively dominant example of the public sector and Los Angeles OCOG (LAOOC) LAOOC as a distinctively dominant example of the private sector sport management approach. Despite the contrasting approaches, both OCOGs have completed realizing an OG from the planning stage to the closing ceremony. The activities of the sports organizations that achieved this success by meeting the expectations of the state and the system to which they belonged demonstrated the importance of focusing on the positive effects on sports management success of the right people taking the steps that meet the needs, rather than a superiority comparison between the requirements of the private or public sector.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".