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Record W3124277044 · doi:10.53637/srqb5157

What Happens When Books Enter the Public Domain? Testing Copyright’s Underuse Hypothesis across Australia, New Zealand, The United States and Canada

2019· article· en· W3124277044 on OpenAlexaboutno aff
Jacob Flynn, Rebecca Giblin, François Petitjean

Bibliographic record

VenueUniversity of New South Wales Law Journal · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsnot available
FundersMonash University
KeywordsPublic domainPublishingCompetition (biology)Project commissioningInvestment (military)Test (biology)EconomicsPublicationIntellectual propertyPublic investmentLaw and economicsLawBusinessAdvertisingPolitical sciencePublic economicsHistoryPublic fund

Abstract

fetched live from OpenAlex

A key justification for copyright term extension has been that exclusive rights encourage publishers to make older works available (and that, without them, works will be ‘underused’). We empirically test this hypothesis by investigating the availability of ebooks to public libraries across Australia, New Zealand, the United States and Canada. We find that titles are actually less available where they are under copyright, that competition apparently does not deter commercial publishers from investing in older works, and that the existence of exclusive rights is not enough to trigger investment in works with low commercial demand. Further, works are priced much higher when under copyright than when in the public domain. In sum, simply extending copyrights results in higher prices and worse access. We argue that nations should explore alternative ways of allocating copyrights to better achieve copyright’s fundamental aims of rewarding authors and promoting widespread access to knowledge and culture.

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)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0040.005
Scholarly communication0.0050.003
Open science0.0020.003
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 head0.057
GPT teacher head0.201
Teacher spread0.145 · 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 designObservational
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

Citations7
Published2019
Admission routes1
Has abstractyes

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