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Record W1856655805 · doi:10.3138/utlj.3205

Disgorgement: From property to contract

2015· article· en· W1856655805 on OpenAlexvenueno aff
Nicholas Sage

Bibliographic record

VenueUniversity of Toronto Law Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and International Contract Law
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessProperty (philosophy)Breach of contractLaw and economicsExclusion clauseLawContract managementEconomicsPolitical scienceDamagesPhilosophy

Abstract

fetched live from OpenAlex

The article develops an understanding of the disgorgement remedy in private law by moving between the proprietary context, where the remedy has long been awarded, and the contractual context, where the remedy is relatively new and still controversial. The resulting account can explain the emerging common law on disgorgement for breach of contract, which has so far eluded explanation. The account also has broader implications for private law theory. First, it suggests that asking whether the plaintiff has a right ‘to a thing’ (the paradigmatic sort of property right) may obscure the remedial analysis. Instead, the analysis should attend to another, hitherto overlooked aspect of the plaintiff’s rights: their logical scope. Second, the account suggests that a purely ‘rights-based’ understanding of private law remedies cannot adequately explain disgorgement, because it elides the crucial role that the defendant’s wrongful action plays in the explanation for the remedy.

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)0.006
metaresearch head score (Gemma)0.013
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 score0.009
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.047
Scholarly communication0.0090.018
Open science0.0020.009
Research integrity0.0090.009
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 head0.034
GPT teacher head0.262
Teacher spread0.227 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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