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Record W3177411616 · doi:10.7202/1077697ar

Le Canada devant ses principes constitutionnels sous-jacents en temps de crise : regards sur la gestion de la COVID-19

2021· article· fr· W3177411616 on OpenAlexvenueaboutno aff
Dave Guénette, Félix Mathieu

Bibliographic record

VenueLes Cahiers de droit · 2021
Typearticle
Languagefr
FieldSocial Sciences
TopicHuman Rights and Immigration
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceCoronavirus disease 2019 (COVID-19)PhilosophyMedicine

Abstract

fetched live from OpenAlex

La pandémie de COVID-19 a engendré des bouleversements majeurs dans de nombreux pays. Or, en temps de crise, il est probable que certaines normes juridiques encadrant le fonctionnement d’une société, de même que les idéaux qui la guident, soient court-circuitées pour répondre à des impératifs plus pressants. C’est notamment ce qu’avait théorisé Kenneth C. Wheare dans son ouvrage phare Federal Government. Fondée sur une logique hypothético-déductive, la présente étude se propose de mesurer l’incidence de la COVID-19 sur la gouvernance au Canada au plus fort de la « première vague » de la pandémie. Concrètement, les auteurs testent l’hypothèse suivante : en temps de crise, les modalités exceptionnelles en fonction desquelles la gouvernance est réorganisée pour un temps donné mettent à mal les principes fondamentaux qui sont censés animer un système politique fédéral. À travers le prisme d’analyse du Renvoi relatif à la sécession du Québec, les auteurs montrent que la gouvernance au Canada, au cours de la première vague de la pandémie de COVID-19, a eu des répercussions différenciées sur les principes constitutionnels sous-jacents du fédéralisme, de la démocratie, du constitutionnalisme et de la primauté du droit, ainsi que de la protection des minorités.

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)0.004
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: Commentary · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.395

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.016
Scholarly communication0.0070.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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
GenreCommentary

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
Published2021
Admission routes2
Has abstractyes

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