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Record W2317789752 · doi:10.1080/09523367.2016.1155559

Marketing Avery Brundage’s Apoplexy: The 1976 Montreal Olympics Self-Financing Model

2016· article· en· W2317789752 on OpenAlexafffundabout
Estée Fresco

Bibliographic record

VenueThe International Journal of the History of Sport · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLotteryContext (archaeology)Ambush marketingPoliticsPrideScholarshipPolitical scienceMarketingFinancePublic relationsSociologyEconomicsAdvertisingBusinessEconomic growthHistory

Abstract

fetched live from OpenAlex

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.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.339

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 science0.0020.000
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 head0.024
GPT teacher head0.257
Teacher spread0.233 · 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

Citations1
Published2016
Admission routes3
Has abstractyes

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