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Neither ‘America’ nor ‘Québec’: constructing the Canadian multicultural nation

2007· article· en· W1975633771 on OpenAlexaboutno aff
Elke Winter

Bibliographic record

VenueNations and Nationalism · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsMainstreamMulticulturalismMelting potNationalismOptimal distinctiveness theoryGender studiesSociologyMythologyPower (physics)Ethnic groupNational identityMedia studiesPolitical scienceAnthropologyHistoryLawPoliticsSocial psychologyPsychology

Abstract

fetched live from OpenAlex

ABSTRACT.Although researchers have deconstructed the myth of stark social differences between the various North American sub‐societies, an assimilating American melting pot and an ethnically oppressive monocultural Québec are still popular representations within Canadian majority discourses, such as the English‐language mainstream media and parts of academia. In this paper, I argue that images of ‘America’ and ‘Québec’ play important roles for the multicultural reconstruction of Canadian nationhood. Examining selected op‐ed articles from two Toronto‐based mainstream newspapers during the 1990s, I develop and exemplify a theoretical understanding of how national identities are constituted and transformed within inter‐ and intra‐national relations of power and alterity. I pay special attention to the particularisation of Canada through the confrontation with American nationhood, the ambiguities of recognising the distinctiveness of Québec inside Canada, and the consequences of projecting Québec's supposedly ‘ethnic’ nationalism outside the boundaries of Canadianness.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.745

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0230.011
Scholarly communication0.0080.002
Open science0.0010.002
Research integrity0.0010.002
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 head0.015
GPT teacher head0.258
Teacher spread0.242 · 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 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

Citations36
Published2007
Admission routes1
Has abstractyes

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