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Record W4395059331 · doi:10.29173/alr2700

The Doctrine of Contractual Absolution

2022· article· en· W4395059331 on OpenAlexaffvenueabout
Marcus Moore

Bibliographic record

VenueAlberta Law Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and International Contract Law
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDoctrineLaw and economicsBusinessEconomicsLawPolitical science

Abstract

fetched live from OpenAlex

The absence of a knowledge requirement is a novel and astonishing feature of unconscionability in Canada, and one that calls for scholarly reflection. In other jurisdictions and formerly in Canada, unconscionability required that the benefiting party knew or at least should have known that its counterpart was impaired in the making of the contract. Such knowledge established a minimum level of wrongdoing, so that even without more active exploitation, it was unconscionable as an “unconscientious abuse of power.” But following the Supreme Court decision in Uber Technologies Inc. v. Heller (2020), Canadian contract law rejects this conventional approach. It does not require exploitation to relieve improvidence by the vulnerable. It is argued here that this does not reflect the notion of unconscionability, and is better understood rather as a novel doctrine of contractual absolution. This article analyzes the important implications of this maverick doctrine for the law, the market, and fundamental assumptions about the nature of contractual obligation.

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)0.014
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 score0.176
Threshold uncertainty score0.351

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.071
Scholarly communication0.0090.006
Open science0.0030.004
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0040.001

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 head0.316
Teacher spread0.296 · 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
Published2022
Admission routes3
Has abstractyes

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