Prevention-intervention strategies to reduce exposure to e-waste
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As one of the largest waste streams, electronic waste (e-waste) production continues to grow in response to global demand for consumer electronics. This waste is often shipped to developing countries where it is disassembled and recycled. In many cases, e-waste recycling activities are conducted in informal settings with very few controls or protections in place for workers. These activities involve exposure to hazardous substances such as cadmium, lead, and brominated flame retardants and are frequently performed by women and children. Although recycling practices and exposures vary by scale and geographic region, we present case studies of e-waste recycling scenarios and intervention approaches to reduce or prevent exposures to the hazardous substances in e-waste that may be broadly applicable to diverse situations. Drawing on parallels identified in these cases, we discuss the future prevention and intervention strategies that recognize the difficult economic realities of informal e-waste recycling.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.021 |
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 it