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Record W4385830852 · doi:10.3138/utlj-2023-0071

The Reconciliation Project of Labour Law

2023· article· en· W4385830852 on OpenAlexaffvenueabout
Supriya Routh

Bibliographic record

VenueUniversity of Toronto Law Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Labor and Employment Law
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLabour lawIndigenousSociologyDemocracyLawCapitalismNarrativeEquity (law)NormativeLaw reformPolitical sciencePrivate lawLaw and economicsPublic lawPolitics

Abstract

fetched live from OpenAlex

Historically, in Canada, labour law has not only failed to protect Indigenous working people from the exclusionary logic of (colonization-induced) market capitalism but also marginalized (peripheral and vulnerable) working people among the settlers. In spite of the Canadian judiciary’s attempt to articulate a broader vision of labour law through the values of justice, liberty, equity, and participatory democracy, the foundational private contractual rationale of labour law acts as a constraint on the judiciary’s ability to develop a broader – and more inclusive – regulatory justification. In this article, I suggest that this tension between expansive normative values and narrow (exclusionary) regulatory justification of labour law could be usefully addressed by employing the idea of reconciliation, originally conceived to fashion the relationship between Indigenous and non-Indigenous peoples in Canada. I argue that an appropriately formulated idea of reconciliation should be able to promote a more inclusive conceptual foundation of labour law, one that is receptive of non-Eurocentric world-views in its foundational narrative and democratic in its continued execution. In so aiding labour law, the reconciliation perspective can close the gap between normative values and regulatory justification of the discipline.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.989
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.024
GPT teacher head0.285
Teacher spread0.261 · 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.

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

Citations0
Published2023
Admission routes3
Has abstractyes

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