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Record W7065907989

Évangéliser les Canadiens français: missions et retraites paroissiales au XIXe siècle

2014· preprint· fr· W7065907989 on OpenAlexaboutno aff

Bibliographic record

VenueÉrudit documents and data repository (Érudit Consortium, University of Montreal) · 2014
Typepreprint
Languagefr
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Government (linguistics)Context (archaeology)HeadlineEconomic Justice
DOInot available

Abstract

fetched live from OpenAlex

Ferretti, Lucia et Christine Hudon, « Évangéliser les Canadiens français : missions et retraites paroissiales au xix e siècle », dans Frédéric Laugrand et Gilles Routhier (dir.),Les missions au Québec et du Québec dans le monde (CIEQ, coll.« Les chantiers de l'Atlas historique du Québec » : www.atlas.cieq.ca),2014, 22 p.Ce document est protégé par la loi sur le droit d'auteur.L'utilisation des services d'Érudit (y compris la reproduction) est assujettie à sa politique d'utilisation que vous pouvez consulter à l'URL http://www.erudit.org/apropos/utilisation.html Tous droits réservés.Centre interuniversitaire d'études québécoises (CIEQ) Dépôt légal (Québec et Canada), 3 e trimestre 2014.ISBN 978-2-921926-38-6 (PDF) Les chercheurs du CIEQ, issus de neuf universités, se rejoignent pour étudier les changements de la société québécoise, depuis la colonisation française jusqu'à nos jours.Leurs travaux s'inscrivent dans trois grands axes de recherche : les populations et leurs milieux de vie, les institutions et les mouvements sociaux et la culture québécoise: diversité, échanges et transmission.Ils privilégient une approche scientifique pluridisciplinaire originale pour comprendre le changement social et culturel dans ses dimensions spatiotemporelles -www.cieq.ca

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)0.003
metaresearch head score (Gemma)0.007
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.153
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.010
Science and technology studies0.0050.001
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0190.003

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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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
GenreOther

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

Citations0
Published2014
Admission routes1
Has abstractno

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