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Making chains that (un)make things: waste–value relations and the bangladeshi rubbish electronics industry

2011· article· en· W2062130633 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeografiska Annaler Series B Human Geography · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsValue (mathematics)Production (economics)BusinessParticipant observationElectronicsValue creationValue captureElectronic wasteCommerceEconomyIndustrial organizationEconomicsEngineeringSociologyWaste managementComputer scienceMicroeconomicsSocial science

Abstract

fetched live from OpenAlex

.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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.229
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it