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Record W3147980819 · doi:10.3828/jlh.2020.17

Knowledge Activists on Health and Safety: Workmen-Inspectors in Metalliferous Mining in Australia 1901–25

2020· article· en· W3147980819 on OpenAlexaboutno aff
Michael Quinlan, David Walters

Bibliographic record

VenueLabour History · 2020
Typearticle
Languageen
FieldPsychology
TopicHistorical Psychiatry and Medical Practices
Canadian institutionsnot available
Fundersnot available
KeywordsProject commissioningPublishingPublic relationsMine safetyPolitical scienceEngineeringManagementLawWaste managementEconomics

Abstract

fetched live from OpenAlex

Worker campaigns for a more direct say in protecting their health and safety are a significant but under-researched subject in labour history. Largely overlooked are the attempts by coalminers in the UK, Australia and Canada to establish mechanisms for representation on health and safety in the 1870s. This push for a voice then spread to New Zealand, France, Belgium and other countries, with unions eventually securing legislative rights to inspect their workplaces a century before workers in other industries gained similar entitlements. In Australia metalliferous miners' unions followed coalminers in initiating a parallel campaign for the right to appoint their own mine-site and district inspectors (known as "check-inspectors") from the late nineteenth century. This article examines the struggle for and activities/impact of workmen-inspectors in Australian metalliferous mines, including adoption of the competing UK–Australian and Continental-European models. It finds the development conforms to a resistance rather than mutual-cooperation perspective with check-inspectors performing the role of "knowledge activists." The article argues this finding is not only relevant to understanding more recent experience of worker involvement in occupational health and safety but also demonstrates the relevance of historical research to contemporary regulatory policy debates and union strategies.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.095
GPT teacher head0.365
Teacher spread0.270 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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