Making chains that (un)make things: waste–value relations and the bangladeshi rubbish electronics industry
Bibliographic record
Abstract
.There is growing empirical and theoretical interest in post‐consumption activity that results in the capture and creation of value from waste in the global economy. This article engages with two dominant approaches to tracing the capture and creation of value, global value chains (GVCs) and global production networks (GPNs), and their shared call to examine waste disposal and recycling. Using non‐participant observation, semi‐structured interviews, and a survey we examine what happens to the products of one of GVCs ‘and GPNs’ paradigmatic industries, electronics, when they are labelled e‐waste and are imported into Dhaka, Bangladesh, as rubbish electronics. Rather than wasting and final disposal predominating, our research documents a substantial rubbish recovery economy that captures and creates value anew. Consequently, we argue that both GVC and GPN approaches must rethink how they theorize the capture and creation of value.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".