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Record W152419847

C-11 Exceptions aux bibliothèques

2012· article· fr· W152419847 on OpenAlexaboutno aff
Olivier Charbonneau

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2012
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophyEthnologySociology
DOInot available

Abstract

fetched live from OpenAlex

L’œuvre numérique, prise dans le ressac constant de la commodification et de la mutualisation, exacerbe les tensions entre les acteurs du milieu culturel. D’un côté, le droit d’auteur semble noyé par le rôle réservé aux exceptions par le législateur et les tribunaux. De l’autre, les institutions d’enseignement et les bibliothèques constituent des partenaires de choix dans l’appropriation de l’univers numérique. Les bibliothèques universitaires québécoises, par exemple, dépensent 60 millions de dollars en ressources documentaires, dont près du deux tiers (2/3) sont octroyées aux seuls documents numériques en 2009-2010 selon la CRÉPUQ. Ainsi, l’œuvre protégée s’embourbe dans le paradoxe qui entoure sa nature, de commodité et de bien public . Sans pour autant négliger les tensions, il devient impératif de chercher l’intersection de toutes les positions. Dans un premier temps, nous traceront les contours du paradoxe numérique selon les théories de l’analyse économique du droit. Ensuite, nous exploreront certaines pistes pour faire converger le paradoxe vers une conceptualisation qui permet l’émergence de marchés numériques. L’objectif est de rétablir le droit d’auteur dans le panthéon des institution culturelles, au profit de tous les concernés de la société civile et d’un foisonnement culturel québécois numérique.

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)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.986
Threshold uncertainty score0.274

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0090.012
Scholarly communication0.0140.008
Open science0.0020.005
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0380.006

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 head0.244
GPT teacher head0.345
Teacher spread0.101 · 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.

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

Citations0
Published2012
Admission routes1
Has abstractyes

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