The Global Challenge of Electronics: Managing the Present and Preparing the Future
Bibliographic record
Abstract
Abstract Home offices, virtual meeting rooms and online training that were admittedly becoming commonplace, are now essentially inescapable. The greater reliance on electronics, and the data and energy storage, however, comes at a cost. Planned obsolescence and the lack of environmentally benign end‐of‐life scenarios, such as reuse and recycle, but also degrade or vanish for organic electronics, are at the root cause of the dramatic increase in global e‐waste. Their impact is unequally distributed with low‐ and medium‐income countries taking the brunt of the harmful environmental and health effects, on their populations. This Perspective article suggests meaningful paths to mitigate the electronics waste crisis, beyond the 3Rs (reduce, reuse, recycle), that include sustainable and eco‐designed electronics while recognizing the limitations of certain tools at the disposal. Massive paradigm shifts along with profound systemic and cultural changes need to occur for the digital revolution's benefits to outweigh its drawbacks, most important of which are its present unsustainable growth. These shifts need to occur on intersectoral, intersectional, and intergenerational dimensions to alleviate the heavily asymmetrical environmental impact (Global‐North vs Global‐South) e‐waste have. Coordinating efforts at the international level will be crucial to build capacity and make an impact.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".