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Record W2288417313 · doi:10.1093/annhyg/mev089

Green Jobs: Definition and Method of Appraisal of Chemical and Biological Risks

2015· article· en· W2288417313 on OpenAlexaffabout
Erwan Cheneval, Marc-Antoine Busque, Claude Ostiguy, Jacques Lavoie, Robert Bourbonnais, France Labrèche, Bouchra Bakhiyi, Joseph Zayed

Bibliographic record

VenueThe Annals of Occupational Hygiene · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailUniversité de Montréal
Fundersnot available
KeywordsHazardRisk analysis (engineering)Work (physics)Risk assessmentControl (management)Hazard analysisBusinessComputer scienceEngineeringComputer securityReliability engineering

Abstract

fetched live from OpenAlex

In the wake of sustainable development, green jobs are developing rapidly, changing the work environment. However a green job is not automatically a safe job. The aim of the study was to define green jobs, and to establish a preliminary risk assessment of chemical substances and biological agents for workers in Quebec. An operational definition was developed, along with criteria and sustainable development principles to discriminate green jobs from regular jobs. The potential toxicity or hazard associated with their chemical and biological exposures was assessed, and the workers' exposure appraised using an expert assessment method. A control banding approach was then used to assess risks for workers in selected green jobs. A double entry model allowed us to set priorities in terms of chemical or biological risk. Among jobs that present the highest risk potential, several are related to waste management. The developed method is flexible and could be adapted to better appraise the risks that workers are facing or to propose control measures.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.143

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 integrity0.0000.000
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 head0.293
GPT teacher head0.426
Teacher spread0.133 · 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 designObservational
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
Published2015
Admission routes2
Has abstractyes

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