Giving Voice to the Precariously Employed? Mapping and Exploring Channels of Worker Voice in Occupational Health and Safety Regulation
Bibliographic record
Abstract
In most contemporary occupational health and safety (OHS) regimes, great emphasis is placed on amplifying worker voice in regulation through worker health and safety representation in the employers’ OHS management system. Historically, these regimes were designed on the assumption that the workers who would use these mechanisms were full-time workers having secure jobs with their current employers. This is manifestly no longer true, posing a serious challenge to the efficacy of these regimes. After setting out the historical context of worker voice in OHS regulation, this paper begins by mapping out eight channels of worker voice based on the combination of three variables: the subject of worker voice, the object of worker voice and the audience for worker voice. The aim of this exercise is not only to provide greater analytical clarity to discussions of worker voice in OHS regulation, but to also open up the discussion about alternative models of worker voice that might better fit the reality of today’s labour market. The paper examines the research on how well precariously employed workers are able to use existing channels of voice and concludes by considering strategies and tactics for amplifying their voice in OHS regulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".