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Record W2162199202 · doi:10.3138/cjh.49.2.225

Win Friends or Make Enemies: Team Canada’s 1978 Diplomatic Mission to Czechoslovakia

2014· article· en· W2162199202 on OpenAlexvenueaboutno aff
Marcel Jesenský

Bibliographic record

VenueJournal of History · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsOffensiveChampionshipAmateurWorld championshipGovernment (linguistics)Political scienceContext (archaeology)Ice hockeyPublic administrationPublic relationsLawManagementGeographyEconomics

Abstract

fetched live from OpenAlex

The Canadian government’s role in funding and managing amateur sports increased dramatically as part of the expansion of the postwar social welfare state. A case study of the government’s involvement in Team Canada’s participation at the 1978 World Hockey Championship reveals the deliberate attempt by Canadian hockey players, government officials, diplomats, and embassies, to improve Canada’s image abroad after it had been damaged by the team’s unsportsmanlike behaviour at the same championship the previous year. Ottawa’s portrayal of this 1978 “charm offensive” as successful helped it justify its role in operating the Canadian national team. This article situates the government’s role in the 1978 World Championship in the context of trends that began in the 1960s, when the government became more involved in sports, and charts a growing awareness of the potential for sport — and especially hockey in the case of Canada — to function not only as an important component of collective identity, but also as an integral part of the nation’s foreign relations strategy.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.532

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0430.008
Scholarly communication0.0060.001
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.025
GPT teacher head0.277
Teacher spread0.252 · 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
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

Citations0
Published2014
Admission routes2
Has abstractyes

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