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Record W2771234070

Globalizing User Rights-Talk: On Copyright Limits and Rhetorical Risks

2017· article· en· W2771234070 on OpenAlexaboutno aff
Donna Craig

Bibliographic record

VenueeYLS (Yale Law School) · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsnot available
Fundersnot available
KeywordsRhetoricLaw and economicsFair useTerminologyPolitical scienceRhetorical questionLawSociology
DOInot available

Abstract

fetched live from OpenAlex

Around the world, the focus of copyright policy reform debates is shifting from the protection of copyright owners' rights towards defining their appropriate limits.There is, however, a great deal of confusion about the legal ontology of copyright "limits," "exceptions," "exemptions," "defenses," and "user rights."While the choice of terminology may seem to be a matter of mere semantics, how we describe and conceptualize lawful uses within our copyright system has a direct bearing on how we delimit and define the scope of the owner's control.Taking seriously the role of rhetoric in shaping law and policy, this Paper critically examines the recent embrace of the language of "users' rights" to frame fair use, fair dealing, and other non-infringing acts.This terminology has been adopted to varying degrees by courts in Canada, Israel, and the United States and is increasingly employed by public interest advocates and policy-makers at the domestic and international level.In this Paper, I ask whether the rise of "user rights," thus cast, is a positive development that will help to rein in some of copyright's excesses, advancing the cause of content users and the public at large-or whether it is, perhaps, something of a false friend.Drawing on lessons from critical legal theory, I caution that "rights" may be a double-edged sword with the potential to undermine or obstruct the public interests, social values, and relationships that should inform copyright's development in the digital age.As a rhetorical tool, "user rights" should therefore be wielded carefully if public interest advocates are to avoid self-inflicted injury.

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)0.038
metaresearch head score (Gemma)0.052
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 score0.038
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.004
Science and technology studies0.0120.097
Scholarly communication0.0240.048
Open science0.0020.017
Research integrity0.0100.013
Insufficient payload (model declined to judge)0.0080.001

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 head0.056
GPT teacher head0.282
Teacher spread0.226 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

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