MétaCan
Menu
Back to cohort
Record W1591818506 · doi:10.1080/14729342.2015.1047655

Good Faith Duties in Contract Performance

2014· article· en· W1591818506 on OpenAlexaboutno aff
Jeannie Paterson

Bibliographic record

VenueOxford University Commonwealth Law Journal · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsFair dealingLawHonestyGood faithObligationBad faithLoyaltyPower (physics)FaithUnconscionabilityBusinessLaw and economicsPolitical scienceSociology

Abstract

fetched live from OpenAlex

There has been an ongoing debate in common law countries about the merits of recognising a general obligation to act in good faith in the performance of contracts. Courts in England, Australia, Canada and Singapore have responded differently to this possibility. However, courts in those jurisdictions have been prepared to imply a similar bundle of more specific duties that are increasingly seen as expressing the core content of any general principle of good faith in contract law. The ‘good faith’ duties promote loyalty or fidelity to the contractual relationship, primarily by requiring honesty and cooperation in contract performance and by precluding the exercise of discretionary contractual powers in a manner that is unreasonable or outside the proper purposes of the power. The presence of these duties in the relatively stable and commercially orientated contract law of the jurisdictions considered suggests that ideas of good faith need not produce undue uncertainty for contracting parties. On the other hand, the increasingly well-established operation of these more specific duties raises the question of what would be gained through recognising a general obligation of good faith performance in the common law of contract.

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)0.023
metaresearch head score (Gemma)0.038
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 score0.023
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.060
Scholarly communication0.0100.008
Open science0.0020.010
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0060.001

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 head0.016
GPT teacher head0.253
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 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

Citations52
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueOxford University Commonwealth Law JournalSame topicLegal principles and applicationsFrench-language works237,207