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Record W7161811620 · doi:10.82308/28080

Droit d’auteur et encouragement de la connaissance

2020· dissertation· fr· W7161811620 on OpenAlexaboutno aff
Daniel Goldenbaum

Bibliographic record

Venuenot available
Typedissertation
Languagefr
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsPublic domainLegislatorStatuteIdeal (ethics)State (computer science)Copyright lawFacet (psychology)

Abstract

fetched live from OpenAlex

How is the relationship between copyright and knowledge articulated and what influence does it have on the copyright regime? These are the main questions this study attempts to answer. The notion of knowledge has been and continues to be a driving force behind copyright developments. It has been invoked since the earliest legislations and has become one of its fundamental justifications. It structures copyright as we understand it. The first modern copyright law, the Statute of Anne enacted in 1710 in England, is imbued with the ideal of encouraging knowledge. The Canadian stance also provides a glaring example of the dialogue between copyright and knowledge. In the 19th century, the Canadian colony sought to promote its own culture. It perceived and used copyright as a means of educating its people and structured its regulations to reflect the state of learnedness in its territory. In 2012, the adoption of fair dealing for educational purposes exposed a new facet of the relation at hand. Until then, the grant of exclusive rights had been considered the main tool for promoting education, if not its raison d'être. Today, the Canadian legislator finds the (relative) public domain of education more appropriate. This thesis proposes a renewed understanding of copyright through the prism of its relationship with knowledge. It highlights the arsenal of legal means available for copyright to target the creation, diffusion, dissemination and, very recently, access to knowledge. It invites us to acknowledge the societal motives that govern the choice of such means. The mechanisms introduced and their evolution reflect the socio-economic priorities and the technological context in which they are adopted

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0740.007

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 head0.020
GPT teacher head0.256
Teacher spread0.235 · 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; both teacher heads agree on what is shown here.

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
Published2020
Admission routes1
Has abstractyes

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Same topicCopyright and Intellectual PropertyFrench-language works237,207