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Record W4285283200 · doi:10.7202/1088256ar

Les mots et les maux des réformes de la justice civile

2022· article· fr· W4285283200 on OpenAlexaffvenueabout
Hélène Piquet

Bibliographic record

VenueLes Cahiers de droit · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicArtificial Intelligence in Law
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceEconomic JusticePhilosophyLaw

Abstract

fetched live from OpenAlex

La présente étude porte sur les réformes de la justice civile en cours au Québec telles qu’elles ont été élaborées en 2018 et en 2019 dans les plans du ministère de la Justice. Elles reposent sur des fondements externes au droit : la logique managériale, l’innovation et l’utilisation accrue des technologies de l’information et des communications (TIC). La rhétorique qui sous-tend les réformes juridiques compte. Elle leur imprime une teneur précise qui touche tant les modalités que les finalités de la justice. Ainsi, la volonté affirmée du ministère de la Justice de favoriser l’accès à la justice se trouve partiellement démentie. En outre, les réformes comportent le risque d’une « déspécification » de la justice. Depuis le mois de mars 2020, la pandémie de COVID-19 exerce une forte contrainte sur la mise en oeuvre des réformes. À l’instar de ce qui se produit dans d’autres juridictions, les cours relèvent maints défis et développent progressivement de nécessaires balises à l’usage des TIC.

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)0.005
metaresearch head score (Gemma)0.017
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: Other · Consensus signal: Other
Teacher disagreement score0.664
Threshold uncertainty score0.677

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0110.014
Scholarly communication0.0120.004
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0150.002

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 head0.035
GPT teacher head0.338
Teacher spread0.303 · 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

Citations1
Published2022
Admission routes3
Has abstractyes

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Same venueLes Cahiers de droitSame topicArtificial Intelligence in LawFrench-language works237,207