Worker health in formal electronic waste recycling plants
Bibliographic record
Abstract
Purpose This article explores the protective measures and the occupational health and safety (OHS) prevention strategies in place in the formal electronic equipment recycling (e-recycling) industry, more specifically in the Greater Montreal area (Quebec, Canada) and their consequences: health inequalities and level of compliance with environmental standards. Design/methodology/approach Semi-structured interviews were conducted using two respondent-specific questionnaires, one for workers and one for supervisors. Data collection and analytic procedures drew from qualitative content analysis. It was tempted to identify differences in OHS practices in relation to the workers' employment status and to link the companies' OHS concerns to their level of compliance with environmental standards. Findings The article highlights specific OHS issues in the formal e-recycling industry. Enforcing compliance with environmental standards as a lever for promoting OHS appears to be a promising strategy. Another main finding was the workforce diversity and related OHS vulnerabilities in this industry and the challenges they pose to employers' ability to adequately and equally reach and protect all workers involved. Originality/value To date, too little attention appears to have been paid to working conditions and worker protection in this rapidly growing sector. Specific prevention programmes could be implemented and adapted to the industry's diverse workforce and its multiple OHS vulnerabilities. This issue calls for the international community to take responsibility, as many electronic waste (e-waste) generated worldwide is shipped to developing countries, where lack of regulation and control is much more striking in a sector that remains very largely informal.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".