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Record W2769223758 · doi:10.25071/ryr.v3i0.40405

“Steak, Blé d’Inde, Patates”: Eating National Identity in Late Twentieth-Century Québec

2016· article· en· W2769223758 on OpenAlexaboutno aff
Sandra Roy

Bibliographic record

VenueRevue YOUR Review (York Online Undergraduate Research) · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsNational identityNationalismIdentity (music)ReferendumMythologyHistoryLyricsGender studiesEthnologyGastronomySymbol (formal)SociologyArtLiteraturePolitical scienceLinguisticsAestheticsTourismClassicsLaw

Abstract

fetched live from OpenAlex

This paper explores the connections between  pâte chinois  and Quebec national identity during the second half of the twentieth century. The respective French, British, and Native roots of the ingredients are highlighted and discussed, with a particular emphasis on socioeconomic and cultural terms that also extends to the analysis of the historical preparation of the layered meal, more akin to “daily survival” than to gastronomy. Special attention is also given to the significance of the dish’s origin myths, as well as to cultural references on a popular television series. Those origin myths are separated along the French/English divide, thus evoking the often-tempestuous relationship between these two languages and their speakers in Quebec. The progression of the discourse surrounding  pâte chinois , from a leftover dish prior to the rise of nationalism in the ’70s, to a media darling in the decade following the 1995 referendum, corresponds with efforts to define and then to redefine Quebecois identity. The history of the dish tells the tumultuous history of the people of Quebec, their quest for a unique identity, and the ambiguous relationship they have with language.  Pâte chinois  became a symbol, reminding French Canadians of Quebec daily of their Quebecois identity .

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.101
GPT teacher head0.357
Teacher spread0.257 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueRevue YOUR Review (York Online Undergraduate Research)Same topicCulinary Culture and TourismFrench-language works237,207