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Record W2082607229 · doi:10.7202/1014846ar

Territoires-ressources, migrations et minorités linguistiques : le cas de deux régions périphériques canadiennes

2013· article· fr· W2082607229 on OpenAlexaffvenueabout
Maurice Beaudin, Éric Forgues, Josée Guignard Noël

Bibliographic record

VenueMinorités linguistiques et société · 2013
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Le présent texte traite des migrations dans deux régions canadiennes qui comptent une importante minorité francophone. Leur situation géographique quelque peu excentrée par rapport aux grands centres du pays ainsi que leur dépendance envers l’exploitation des ressources naturelles en font depuis longue date des régions d’émigration au profit surtout des centres urbains provinciaux du Sud. Quelle est l’ampleur de ces flux ? Peut-on y déceler des tendances lourdes ? Dans quelle mesure les jeunes sont-ils touchés par les flux migratoires ? Où vont les migrants ? Autant de questions qui nous permettront de voir en quoi les migrations influent sur la vitalité économique ainsi que la vitalité démolinguistique des régions à l’étude.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0130.006
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.020
GPT teacher head0.310
Teacher spread0.290 · 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
Published2013
Admission routes3
Has abstractyes

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