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Record W4290458058 · doi:10.4000/eccs.5525

Le castor et le goupillon. Pierre Deffontaines et le Canada, ou appréhender l’espace politique, social et culturel par la géographie humaine

2022· article· fr· W4290458058 on OpenAlexaboutno aff
Antoine Huerta

Bibliographic record

VenueÉtudes canadiennes / Canadian Studies · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicMilitary, Security, and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

La pratique géographique de Pierre Deffontaines dans les contrées canadiennes à partir de 1948 se fonde sur une connaissance singulière du terrain ; la géographie régionale qu’il met alors en place lors de ses missions successives en porte la marque. Sa relation au Canada et spécialement au Canada français, « repose sur sa capacité d’émerveillement » sur toutes les caractéristiques politiques, sociales et culturelles des espaces qui l’entourent : rien de ce qui est humain ne lui est étranger. Ce texte propose de traiter la façon dont le géographe utilise ses réseaux catholiques pour mener à bien ses recherches et obtenir des renseignements. Ses informateurs, rencontrés en compagnie de divers collègues de l’université Laval à Québec, dont Luc Lacourcière, constituent sa source scientifique première. Appréhender la construction du territoire dans le contexte canadien de l’après Seconde Guerre mondiale passe par la compréhension de certains des hommes qui en écrivirent la géographie : Deffontaines fait partie de ceux-là. Alors même que ses travaux sur le rang d’habitat comme fait géographique total sont relativement oubliés, il apparaît néanmoins qu’ils représentent une géographie humaine et régionale canadienne tout à fait particulière que nous présenterons.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.052
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0140.013
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0180.001

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 head0.029
GPT teacher head0.292
Teacher spread0.262 · 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 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
Published2022
Admission routes1
Has abstractyes

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