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Record W3125842437 · doi:10.7202/1025139ar

Property in Licences and the Law of Things

2014· article· en· W3125842437 on OpenAlexvenueno aff
Christopher Essert

Bibliographic record

VenueMcGill Law Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsProperty rightsProperty (philosophy)Optimal distinctiveness theoryLaw and economicsProperty lawTangible propertyLawGovernment (linguistics)Bundle of rightsPublic propertyNumerus claususRight to propertyIntangible propertyReservation of rightsPolitical scienceFundamental rightsBusinessSociologyHuman rightsPhilosophyEpistemology

Abstract

fetched live from OpenAlex

A theoretical account of property rights needs to identify what, if anything, is distinctive about property rights as opposed to other sorts of rights; what makes them the sorts of rights that they are. An important and prominent account of the distinctiveness of property rights claims that they are rights to things. I argue against this view: I show that a government-issued licence (to fish or to drive a taxi or to operate a radio station, say) is not a right to a thing but should nevertheless count as a property right. I consider two different arguments for this rights-to-things view: one is based on the Hohfeldian structure of property rights, and one relies on the importance of information costs in the law of property. While each of these arguments teaches us important lessons about property, none can properly support the conclusion that property is rights to things. I suggest that abandoning the rights-to-things view of property can lead to important insights into property theory more generally.

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)0.007
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.039
Scholarly communication0.0080.014
Open science0.0010.004
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0100.001

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 head0.020
GPT teacher head0.275
Teacher spread0.255 · 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 designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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