The changing geography of global trade in electronic discards: time to rethink the e‐waste problem
Bibliographic record
Abstract
This paper provides a synopsis of the changing geography of global trade in electronic waste over time using data available from the U nited N ations COMTRADE database. It quantifies the magnitude and direction of this trade between 206 territories in over 9400 reported trade transactions between 1996 and 2012. The results demonstrate two key findings. First, at its peak in 1996, trade from territories designated as A nnex VII under the Basel Convention (‘developed’ countries) to non‐ A nnex VII territories (‘developing’ countries) accounted for just over 35% of total trade. By 2012 trade from A nnex VII to non‐ A nnex VII territories accounted for less than 1% of total trade. Second, between 1996 and 2012 the two groups of territories evolved different regional trade orientations: A nnex VII territories are predominantly trading intra‐regionally, with 73–82% of total trade moving between A nnex VII territories. In contrast, non‐ A nnex VII territories are mostly trading inter‐regionally: by 2012 less than one‐quarter of non‐ A nnex VII trade moved to other non‐ A nnex VII territories with the rest moving to A nnex VII territories. The results are congruent with an emerging body of research that profoundly troubles the dominant conceptual and policy framings of the global e‐waste problem. Solving that problem will not happen if it is imagined as one predominantly characterised by dumping of e‐waste from rich, ‘developed’ countries of the ‘global North’ in poor, ‘developing’ countries of the ‘global South’. A reframing of the issue of e‐waste is necessary to productively enrich the conceptualisation and policy discussion of e‐waste as an issue of environmental and economic politics and justice.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".