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Record W4321602674 · doi:10.1515/til-2023-0011

Good faith in employment

2023· article· en· W4321602674 on OpenAlexaff
Sabine Tsuruda

Bibliographic record

VenueTheoretical Inquiries in Law · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Law and Human Rights
Canadian institutionsQueen's University
Fundersnot available
KeywordsDutyFair dealingLaw and economicsContext (archaeology)Meaning (existential)Scope (computer science)BusinessLawBad faithTransformative learningPolitical scienceGood faithSociology

Abstract

fetched live from OpenAlex

Abstract This Article argues that the duty of good faith in contractual performance offers powerful but neglected resources to empower workers to pursue their legitimate interests and resist mistreatment by employers. The duty of good faith creates a joint authority structure within contractual relationships, vesting co-contractors with equal and joint authority over the meaning, purposes, and, hence, the requirements of their contract. Implementing such an authority structure requires ensuring that the parties to a contract have the communicative space and epistemic resources they need to uncover and develop a common understanding of their contract. In the context of employment, such an authority structure would be transformative. It would require legal recognition of a variety of employee speech rights and protection from termination for reasonable and good faith refusals to perform work, and would offer a legal basis to challenge the scope and enforceability of at-will employment clauses. The duty of good faith could thus supply a common law foundation for rights and obligations commonly associated with labor law.

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)0.007
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.050
Scholarly communication0.0080.005
Open science0.0010.007
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.025
GPT teacher head0.255
Teacher spread0.229 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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