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Record W2554620543 · doi:10.1057/978-1-137-46492-7_11

“The Olympics Do Not Understand Canada”: Canada and the Rise of Olympic Protests

2016· book-chapter· en· W2554620543 on OpenAlexaffabout
Christine O’Bonsawin

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGermanAmbush marketingPolitical scienceGovernment (linguistics)FeelingAnti-AmericanismGerman governmentHistoryMedia studiesEconomic historyLawSociologyPsychology

Abstract

fetched live from OpenAlex

The selection of Montreal as host of the 1976 Olympic Summer Games appeared to be a straightforward decision for the International Olympic Committee (IOC). After all, Montreal had previously hosted a highly successful 1967 International and Universal Exposition—Expo ’67—and Canada had showed a remarkable aptitude for staging international and domestic multi-sport events. Moreover, the city of Montreal has long been heralded as “the cradle of Canadian sport.” 1 Nonetheless, in the lead-up to the 1976 Olympic Games, amidst gloomy headlines about soaring deficits, extreme security measures, construction delays, and deaths, and in the midst of a national unity crisis, many Canadians seemingly lost interest, or quite simply opposed the arrival of the Olympic Games on national soil. An East German editor went so far as to question “[w]hy did Canada want the Games? Why did your federal government agree to them if it didn’t want to help pay?” 2 In responding to the East German’s query, Canadian sportswriter Doug Gilbert suggested that “[t]he Olympics do not understand Canada and Canada does not understand the Olympics. As a result, 22 million of us have been going around for five years with the vague feeling something is wrong with the 1976 Games.” 3 Something was certainly wrong with Montreal, and perhaps something was seriously wrong with Olympic hosting in Canada, more generally. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.102
Threshold uncertainty score0.739

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0340.014
Scholarly communication0.0140.005
Open science0.0020.004
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0150.001

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 head0.019
GPT teacher head0.233
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes2
Has abstractyes

Explore more

Same venuePalgrave Macmillan UK eBooks→Same topicSports, Gender, and Society→French-language works237,207→