Marketing Avery Brundage’s Apoplexy: The 1976 Montreal Olympics Self-Financing Model
Bibliographic record
Abstract
This paper examines the programmes that helped finance the 1976 Montreal Summer Olympic Games. Two of these programmes – the sponsorship, licensing and supplier programme, and coin and stamp programmes – raised far less money than expected. I identify two interrelated factors to explain why sales of Olympic commodities were low. First, the Organizing Committee for the Montreal Olympic Games’ (COJO) self-financing model offered Canadians the opportunity to support the Olympics by purchasing commodities. However, consumers were not yet familiar with cause-related marketing campaigns. Second, COJO could not draw a link between the Games and national pride because French and English Canada were divided over linguistic and cultural differences. Moreover, I argue that the eventual success of the Olympic lottery had less to do with the Olympics and more to do with the fact that it was the first legal national lottery in Canada. By placing the Montreal Olympics within a political, economic and sociocultural context, I highlight the reasons why COJO’s attempt at self-financing failed, thus adding to scholarship on the history of Olympic-related commercial practices and public–private financing models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".