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Record W3199187393 · doi:10.1017/s0008197321000775

MAKING SENSE OF MESNE PROFITS: REMEDIES

2021· article· en· W3199187393 on OpenAlexaff
Charles Mitchell, Luke Rostill

Bibliographic record

VenueThe Cambridge Law Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsTrinity College
Fundersnot available
KeywordsRestitutionUnjust enrichmentDamagesTortDutyBreach of contractLawDebtBusinessLaw and economicsEconomicsPolitical scienceLiabilityFinance

Abstract

fetched live from OpenAlex

Abstract This is the second of two articles about cases in which awards of “mesne profits” have been made against defendants who have occupied claimants’ land. The first article argues that the facts of cases where such awards have been made variously support claims in tort, contract or unjust enrichment and that practical consequences can flow from categorising the cases in one way or another. One is that different rules affect the assessment of remedies awarded to claimants depending on the claim that was made and the remedy that was awarded. The present article develops this point by examining the assessment principles governing “mesne profits” awards, according to whether these are classified as compensatory damages in tort, restitutionary damages in tort, orders that a defendant perform a contractual duty to pay a debt, compensatory damages for breach of contract, or orders that a defendant make restitution of an unjust enrichment.

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)0.010
metaresearch head score (Gemma)0.026
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: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0060.033
Scholarly communication0.0100.007
Open science0.0020.009
Research integrity0.0090.008
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 head0.071
GPT teacher head0.362
Teacher spread0.291 · 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
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

Citations34
Published2021
Admission routes1
Has abstractyes

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