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Record W1965668312 · doi:10.3366/ccs.2007.4.2.283

Writing the Heartland II: Monique LaRue and <i>la Côte Nord</i>

2007· article· en· W1965668312 on OpenAlexaboutno aff
Ceri Morgan

Bibliographic record

VenueComparative Critical Studies · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsFrenchIdeologyEliteHistoryNationalismScotsEthnologyHumanitiesSociologyGender studiesAnthropologyMedia studiesArtPolitical scienceLiteraturePoliticsLaw

Abstract

fetched live from OpenAlex

This article comes out of a project entitled Heartlands/Pays de cœur, which looks at representations of places outside of Montreal in contemporary francophone Quebecois and Canadian fi ction. Although the last ten years or so have seen an increase in examinations of the spatial in francophone Quebecois fi ction, apart from a few exceptions, analyses of representations of the rural tend to be limited to specifi c book chapters, or else focus on le roman du terroir (the novel of the land).1 This was a major literary genre from the middle of the nineteenth century to the 1940s, and celebrated the French-Canadian nationalist ideologies of Catholicism, an attachment to the land, and la revanche des berceaux (literally ‘the revenge of the cradles’, that is, maintaining a high birth rate). These were considered vital to the survival of French-Canadian culture and society. Largely because Montreal is the economic and cultural centre of the province, francophone – and anglophone – Quebecois literature is primarily identifi ed with this city. The prevalence of Montreal in francophone cultural production is also attributable to its particular signifi cance within the Quebecois imaginary. By virtue of its historical association with an anglophone – primarily Anglo-Scots – business and social elite, Montreal has been seen as both home and yet not home for francophone Quebeckers.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.107
GPT teacher head0.416
Teacher spread0.309 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2007
Admission routes1
Has abstractyes

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