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Record W2772060470 · doi:10.7202/1042166ar

Les « régions carrefours » du moyen nord comme laboratoires interculturels de nordicité

2017· article· fr· W2772060470 on OpenAlexaffvenueabout
Étienne Rivard, Caroline Desbiens, Suzy Basile, Laurie Guimond

Bibliographic record

VenueRecherches sociographiques · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversité du Québec à MontréalUniversité du Québec en Abitibi-TémiscamingueUniversité LavalUniversité de Saint-Boniface
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Contrairement à ce que peut laisser supposer la définition du Plan Nord, la limite entre l’espace nordique et le Québec laurentien ou méridional n’a rien d’une ligne continue et invariable. Fruit d’un ensemble de critères physiques et humains connexes, cette limite tient davantage de la zone de transition dynamique que de la ligne frontière. Plusieurs régions du Québec, qu’on connaît généralement sous les vocables de « régions périphériques » ou « régions ressources », se trouvent, du moins en partie, à l’intérieur de cette zone. À notre avis, elles sont des « régions carrefours » pouvant assurer un lien concret entre le Nord et l’axe laurentien et qui permettent de mieux comprendre la place du Nord dans l’ensemble québécois et ainsi, de redéfinir la nordicité (ou le caractère nordique) de la province. L’objectif principal de cet article est de vérifier la pertinence de cette hypothèse. En prenant appui sur des entretiens semi dirigés auprès d’acteurs régionaux allochtones – enquêtes qui visaient à mesurer la place des populations autochtones dans les stratégies de développement et de partenariat au Saguenay-Lac-Saint-Jean et en Abitibi-Témiscamingue –, nous proposons d’établir une esquisse critique des relations interethniques dans ces deux régions.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.609
Threshold uncertainty score0.777

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.004
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.172
GPT teacher head0.374
Teacher spread0.202 · 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

Citations8
Published2017
Admission routes3
Has abstractyes

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