Electronic recycling plants: human resources and OHS management case studies
Bibliographic record
Abstract
Purpose The electrical and electronic recycling industry is experiencing significant growth while paying no particular attention to the health and safety of recycling workers. Who are these recycling workers? How are they recruited and trained in OHS measures? This article will attempt to answer these questions. Design/methodology/approach As part of a toxicological study carried out on five companies, samples were taken from employees ( n = 100) and their working environment. Among them, 26 workers and six managers also participated in interviews on the management of OHS preventive practices. This article presents analyses of the recruitment strategies for these workers and the management of preventive measures. Findings The main findings were that preventive practices vary according to the company's social mission and recruitment strategy. OHS preventive practices vary among the companies, even though the workers are similarly exposed to multiple contaminants. Precarious employment relationships put these workers in a vulnerable position. Originality/value Although recycling electrical and electronic equipment (e-recycling) has been an ecological and moral concern in Western societies for several decades, occupational health and safety (OHS) management in recycling plants has received little attention.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".