Portrayals in Print: Media Depictions of the Informal Sector’s Involvement in Managing E-Waste in India
Bibliographic record
Abstract
For over a decade, media stories have exposed health and environmental harm caused by informal electronics recycling in less industrialized countries. Greater awareness of these risks helped inform regulations across the globe and the development of recycling standards. Yet, media depictions also shape public perceptions of informal workers and their role in handling electronic waste, or e-waste. This paper examines how mainstream print media describes the informal sector’s involvement in handling e-waste in India, especially as policymakers and other stakeholders currently grapple with how to integrate informal workers into formal, more transparent e-waste management schemes. This study evaluates depictions of the informal sector in print articles from both non-Indian and Indian news media outlets, employing controversy mapping principles and digital research tools. Findings may help inform stakeholder agendas seeking to influence public awareness on how to integrate informal workers into viable e-waste management solutions. Subsequent research based on these results could also help stakeholders understand the actors and networks that shape such media depictions. Results from the dataset show that most news articles describe informal workers negatively or problematically due to activities causing health risks and environmental damage, but usually do not discern which activities in the value chain (e.g., collection, dismantling, metals extraction) represent the greatest risks. Comparatively fewer articles portray informal workers positively or as contributing to e-waste solutions. Most articles also do not explain challenges that arise when working with informal workers. As such, media depictions today often lag behind policy debates and obscure multiple facets—good and bad—of the informal sector’s involvement in managing e-waste. Thus, an opportunity exists for policymakers, manufacturers, and advocacy groups to bridge the gap between current media representations of informal workers’ involvement in e-waste management and policy recommendations surrounding their role.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".