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Record W1514056254 · doi:10.7202/014184ar

Making a Difference

2007· article· en· W1514056254 on OpenAlexaffvenueabout
Alan Hall, Anne Forrest, Alan Sears, Niki Carlan

Bibliographic record

VenueRelations industrielles · 2007
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsLegislaturePublic relationsPower (physics)Work (physics)EnforcementRepresentation (politics)Political sciencePoliticsOccupational safety and healthKnowledge managementSociologyLawEngineeringComputer science

Abstract

fetched live from OpenAlex

This article elaborates the concept of knowledge activism as a way of understanding effective health and safety representation within the current Ontario legal regime of internal responsibility. Based on interviews with unionized health and safety representatives in the auto industry, we suggest that knowledge activism is a form of political activism by worker health and safety representatives that is organized around the strategic collection and tactical use of technical, scientific and legal knowledge. We argue that knowledge activism is more effective with reference to larger scale changes in work processes, workplace organization and technologies, and with reference to occupational health issues. Knowledge activism is conceptualized as an effective adaptation to a legislative regime which involves worker representatives in decisions without providing substantive power or proactive enforcement support.

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)0.008
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.022
Scholarly communication0.0150.017
Open science0.0020.012
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0430.016

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 head0.240
GPT teacher head0.511
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations53
Published2007
Admission routes3
Has abstractyes

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