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Record W2904344486 · doi:10.7202/1054039ar

Des immigrants pour la cause : la logique nationaliste du discours de presse sur l’immigration francophone en Acadie

2018· article· fr· W2904344486 on OpenAlexaffvenueabout
Isabelle Violette

Bibliographic record

VenueFrancophonies d Amérique · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsPolitical scienceFrenchHumanitiesArt

Abstract

fetched live from OpenAlex

Cet article a pour objectif d’examiner les idéologies linguistiques qui sous-tendent et orientent le traitement discursif de l’immigration dans la presse acadienne récente (2000-2014). Alors qu’à l’heure actuelle l’immigration canalise des tensions politiques et exacerbe des conflits nationaux dans de nombreuses sociétés occidentales, elle bénéficie d’une représentation médiatique favorable en Acadie du Nouveau-Brunswick. Or seule l’immigration dite « francophone » est jugée désirable et bénéfique, ce qui suppose une forme de régulation des communautés et des populations impliquées dans le fait français . La presse acadienne relaie principalement un discours militant sur l’immigration qui consiste à en faire une cause à défendre pour le bien commun. L’auteure montrera que l’immigration francophone a bonne presse puisqu’elle est conçue pour servir une idéologie nationaliste de la langue : parler français suppose une allégeance aux communautés acadiennes et un engagement envers l’avenir de celles-ci.

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)0.005
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 score0.195
Threshold uncertainty score0.393

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0130.010
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.000

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 head0.009
GPT teacher head0.238
Teacher spread0.230 · 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

Citations3
Published2018
Admission routes3
Has abstractyes

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