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Record W2976569105 · doi:10.21083/surg.v4i2.1315

The elephant in the (class)room: The debate over Americanization of Canadian universities and the question of national identity

2011· article· en· W2976569105 on OpenAlexvenueaboutno aff

Bibliographic record

VenueSURG Journal · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsAmericanizationAmbivalenceCitizenshipSociologyIdentity (music)National identityGender studiesPolitical scienceMedia studiesLawAestheticsPoliticsPsychoanalysisPsychology

Abstract

fetched live from OpenAlex

Focusing primarily on the period from 1968 to 1970, this essay analyses how a campaign led by two Carleton University professors, Robin Mathews and James Steele, to defeat “Americanization” in Canadian universities, morphed into a crucial nationwide debate. Ultimately, it will find that regardless of academic or social rank or citizenship, all participants in the debate relied on one common idea to support their arguments and criticize their opponents: that of the ‘colonial mentality’, or the notion that Canadians unquestionably accepted their country as subservient to the United States. Ultimately, this paradoxical usage of postcolonial themes represented an underlying ambivalence in regards to what was being debated in the first place. Thus this essay strives to address how a specific dispute within academia could, in Mathews and Steele’s words, evolve into a “struggle for the very existence of Canada as a self-respecting and independent community” [1a]. Moreover, it contributes to a deeper understanding of Canadian-American relations and the recent debate on Canadian universities’ hiring practices, which continues to be an issue nearly forty years later. In doing so it presents a fascinating case study of national identity within postcolonial frameworks.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.983

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0610.056
Scholarly communication0.0130.005
Open science0.0020.004
Research integrity0.0040.006
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 head0.016
GPT teacher head0.242
Teacher spread0.225 · 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.

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

Citations1
Published2011
Admission routes2
Has abstractyes

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