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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 OpenAlex
Hélène Piquet

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.006
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.035
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