Overlapping vulnerabilities in workers of the electronics recycling industry formal sector: A commentary
Bibliographic record
Abstract
Vulnerabilities in workers performing electronics recycling (e-recycling) in the informal sector worldwide have been well documented. However, the growing e-recycling industry in the formal sector still brings many challenges to protect the health of workers and their environment. This commentary aims to draw attention to the overlooked vulnerabilities faced by the workers of the e-recycling industry formal sector in high-income countries and discuss the potential impact on health inequalities experienced by these workers. Expanding the definition of vulnerability, not limited to the biological susceptibility to chemical and physical exposures, the demographic characteristics of workers in the e-recycling formal sector often reveal social groups known to be disadvantaged regarding occupational exposures and health effects, including young workers, immigrant or ethnic minorities, and workers with mental or physical health issues or disabilities. Overlapping structural vulnerabilities of the e-recycling industry stem from its newness, its working conditions, its conditions of employment, and the sociodemographic characteristics of its workforce. This phenomenon in high-income countries is not restricted to the e-recycling industry alone. It is rather a symptom of more generalized macro socioeconomical phenomena. The present challenges are in line with the new gig and green economies and changes in the global market, and their consequences on the solid waste sector. Continued efforts to strengthen the inclusion of social aspects of health into the complex interaction of the structural vulnerabilities met by e-recycling workers will be essential to anticipate and prevent health issues in this essential but still emerging workforce.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.028 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".