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Record W1916116833

Monopoly and Competition in the Collective Administration of Public Performance Rights (in Hebrew)

2006· article· en· W1916116833 on OpenAlexaff
Ariel Katz

Bibliographic record

VenueSSRN Electronic Journal · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCopyright and Intellectual Property
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNatural monopolyMonopolyCompetition (biology)LegislationLaw and economicsAdministration (probate law)EconomicsDigital rights managementMarket powerBusinessMarket economyPublic economicsIndustrial organizationPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

In most countries the right to perform music in public is not administered individually by the copyright holders but collectively by Performing Rights Organizations (PROs). The common explanation for the proliferation of collective administration is that some aspects of copyright administrations are natural monopolies. It is often argued that individual administration is impracticable or at least uneconomical. Collective administration is therefore promoted as the most efficient method for licensing, monitoring and enforcing those rights. In addition, since the market is a natural monopoly, regulation, rather than an attempt to foster competition, is thought to be the optimal regulatory response. This article critically analyzes the various justifications for collective administration. It argues that the case for PROs is not as straight forward as it is assumed to be, and shows that many of the underlying cost efficiencies that are attributed to PROs are usually simply assumed, and in many cases could be equally achieved under less restrictive arrangements. The article also shows that the existence of new technologies - the Internet, Digital Rights Management Technologies, and advanced monitoring technologies - undermines the case for collective administration even further. Also examined are two models for the regulation of PROs that have been recently proposed in Israel, an antitrust model and a specific legislation model. The models are examined from the aspects of their ability to restrain PROs' market power and to facilitate transition from monopoly to competition.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.201
Teacher spread0.188 · 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 teacher head, 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
Published2006
Admission routes1
Has abstractyes

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