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Record W3015089011 · doi:10.3138/utlj.2019-0130

The capabilities approach: A panacea for labour law’s ills?

2020· article· en· W3015089011 on OpenAlexvenueno aff
David Cabrelli

Bibliographic record

VenueUniversity of Toronto Law Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipSubject (documents)PoliticsPanacea (medicine)Labour lawSociologyLawLaw and economicsFoundation (evidence)EpistemologyPolitical scienceComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

In recent years, there has been a tendency to view the subject of labour law and its goals through the prism of philosophy, and political philosophy in particular. A veritable ‘cottage industry’ has sprung up devoted to this task. In this paper, I analyze the arguments presented by various authors in a book recently published under the editorship of Professor Brian Langille on The Capability Approach to Labour Law, which makes an important contribution to labour law scholarship. I make the point that the capabilities approach – a theory belonging to political philosophy – is undoubtedly a useful instrument insofar as it enables labour lawyers to sell the claims and arguments that the subject is attempting to make, but that if it is presented as an ostensibly univocal foundation, it is unlikely to stack up. Instead, by abstracting its partial and pluralist base, capabilities can be reconceptualised as only one justification for particular labour laws, serving alongside many other political philosophies. In this way, a veritable patchwork quilt of different foundations for specific labour laws can be stitched together. This paper then goes on to make some preliminary observations as to how such a fabric could be embroidered in a coherent and systematic manner.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.210
Teacher spread0.192 · 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 teacher head, not a consensus.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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