MétaCan
Menu
Back to cohort
Record W3012844577

Le droit d’auteur canadien à l’ère de l’intelligence artificielle : le cas du traitement du langage naturel

2020· article· fr· W3012844577 on OpenAlexaboutno aff
Hélène Beauchemin

Bibliographic record

VenueLes Cahiers de propriété intellectuelle · 2020
Typearticle
Languagefr
FieldComputer Science
TopicLaw, AI, and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

L’intelligence artificielle bouleverse tous les secteurs et comme toute innovation, elle amene son lot d’incertitudes. Le droit d’auteur dans sa forme actuelle n’est pas adapte aux processus technologiques utilises par l’intelligence artificielle et constitue un obstacle a la pleine realisation de son potentiel. Le texte suivant vise a dresser un portrait global des difficultes d’interpretation de certaines dispositions de la Loi sur le droit d’auteur applique au contexte specifi que du traitement et de la comprehension du langage naturel.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0040.015
Scholarly communication0.0140.012
Open science0.0010.005
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0180.004

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 head0.019
GPT teacher head0.216
Teacher spread0.197 · 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 designTheoretical or conceptual
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueLes Cahiers de propriété intellectuelleSame topicLaw, AI, and Intellectual PropertyFrench-language works237,207