MétaCan
Menu
Back to cohort
Record W4404808060 · doi:10.7202/1114173ar

Jean-Noël Guertin (1889-1977) : architecte autodidacte

2024· article· fr· W4404808060 on OpenAlexvenueno aff
Robert B. Perreault

Bibliographic record

VenueRabaska Revue d ethnologie de l Amérique française · 2024
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

Lorsque le curé Jean-Noël Guertin du village de Saint-Casimir baptise son petit- neveu, lui donnant son propre nom, il dit aux parents du bébé : « Je souhaite qu’il me remplace. » Toutefois, le jeune Jean-Noël aura d’autres idées. Au lieu de devenir chef spirituel de l’église du village, il ira ailleurs pour créer des plans architecturaux d’églises. Élevé dans un environnement où règne l’industrie du bois, il travaillera pour son père, ouvrier et propriétaire d’un moulin à scie. Convaincu que cet emploi ne lui offre aucun avenir, il suivra la vague d’émigration québécoise vers la Nouvelle- Angleterre où, à part son métier d’ouvrier en construction et de menuisier, il entreprendra une carrière d’architecte sans scolarité formelle.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.145
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.004
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.022
GPT teacher head0.261
Teacher spread0.239 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueRabaska Revue d ethnologie de l Amérique françaiseSame topicCanadian Identity and HistoryFrench-language works237,207