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Record W3205252510 · doi:10.3138/utlj-2020-0137

Remedial consistency in private law

2021· article· en· W3205252510 on OpenAlexvenueno aff
Peter Jaffey

Bibliographic record

VenueUniversity of Toronto Law Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsRemedial educationConsistency (knowledge bases)DutyWrongdoingCompensation (psychology)Constructive trustLawSequential consistencyLaw and economicsPolitical scienceConsistency modelComputer scienceEconomicsPsychologySocial psychologyData consistency

Abstract

fetched live from OpenAlex

This article is concerned with the concept of ‘remedial consistency,’ the consistency of remedial rights with primary rights in the sense I explain. I argue that the requirement of remedial consistency has important implications across private law. It suggests that the ‘continuity thesis’ does not provide a justification for the right to compensation for a wrong, and I argue that rights to compensation are not generally based on wrongdoing. I also consider whether the absence of a right to specific performance is consistent with the existence of a duty of performance, and I discuss the need for alternative remedies to be mutually consistent. I also discuss the implications of remedial consistency for the concept of unjust enrichment, and I argue on the basis of remedial consistency for the general availability of proprietary claims for invalid transfers.

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)0.022
metaresearch head score (Gemma)0.050
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: Other · Consensus signal: Other
Teacher disagreement score0.022
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.050
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.040
Scholarly communication0.0090.013
Open science0.0020.008
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.256
Teacher spread0.238 · 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
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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