Electronic Waste and Public Health: A Call to Protect Pregnant Women and Children
Bibliographic record
Abstract
Electronic waste (e-waste) poses a critical and growing threat to public health, especially for pregnant women and children. With 62 million tonnes of e-waste generated globally in 2022, less than a quarter was formally recycled. This crisis disproportionately impacts low- and middle-income countries (LMICs), where regulatory frameworks are lacking. Toxic substances like lead and mercury from informal recycling practices cause significant health risks, including adverse neonatal outcomes and developmental impairments. Developed nations employ advanced recycling technologies and strict policies, but these models are not easily adaptable to LMICs. Tailored interventions, such as community-based recycling initiatives, are urgently needed. The Journal of Women and Child Health can champion this cause by publishing research and comparative studies on cost-effective solutions to mitigate e-waste risks. Let us advocate for better futures for vulnerable populations by addressing this escalating crisis.
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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 | 0.017 | 0.039 |
| 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 | 0.011 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.035 | 0.027 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".