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Record W2624789775 · doi:10.54648/ijcl2017003

On Writing Labour Law History: A Reconnaissance

2017· article· en· W2624789775 on OpenAlexaff
Eric Tucker

Bibliographic record

VenueInternational Journal of Comparative Labour Law and Industrial Relations · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsYork University
Fundersnot available
KeywordsLabour lawHistoriographyScholarshipLawSociologyLegal historyPolitical scienceLaw and economicsEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Labour law historians rarely write about the theoretical and methodological foundations of their discipline. In response to this state of affairs, this article adopts a reconnaissance strategy, which eschews any pretense at providing a synthesis or authoritative conclusions, but rather hopes to open up questions and paths of inquiry that may encourage others to also reflect on a neglected area of scholarship. It begins by documenting and reflecting on the implications of the fact that labour law history sits at the margins of many other disciplines, including labour history, legal history, labour law, industrial relations and law and society, but lacks a home of its own. It next presents a short historiography of the writing of labour law history, noting its varied and changing intellectual influences. Next the article notes some of the methodological consequences of different theoretical commitments and discusses briefly the possibilities opened up by computer technologies as revealed by two interesting projects that rely heavily on the construction of sophisticated data bases. Finally, the article reflects on the methodological challenges the author has experienced in his current project on labour law’s recurring regulatory dilemmas and conclude with some thoughts on the contribution labour law history can make to our understanding of the dynamics that shape the law's current challenges.

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: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.936

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.131
GPT teacher head0.376
Teacher spread0.245 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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