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Record W3004969498 · doi:10.3390/admsci10010007

The Social Cost of Informal Electronic Waste Processing in Southern China

2020· article· en· W3004969498 on OpenAlex
Anthony E. Boardman, Jeff Geng, Bruno Lam

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

VenueAdministrative Sciences · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsCentre for Social InnovationImpactUniversity of British Columbia
Fundersnot available
KeywordsChinaPer capitaInformal sectorPer capita incomeEstimationElectronic wasteEconomicsAgricultural economicsBusinessNatural resource economicsEconomic growthWaste managementEnvironmental healthPopulationEngineeringGeographyMedicine

Abstract

fetched live from OpenAlex

Large amounts of e-waste are processed “informally” in lower income countries. Such processing releases dangerous pollutants, which increase mortality and reduce cognitive functioning. This paper estimates the social cost of informal e-waste processing in Southern China. This parameter may be “plugged-in” to cost-benefit analyses that evaluate the economic efficiency of policies to reduce informal e-waste processing in China or other lower income countries. It may also be used in the estimation of the social benefits (or costs) of new or proposed e-waste processing policies in higher income countries. We estimate that the social cost of informal e-waste processing in Guiyu is about $529 million. This amount is equivalent to about $423 per tonne (in 2018 US dollars) or $3528 per person, which is over half the gross income per capita of the residents of Guiyu. We also perform sensitivity analysis that varies the estimated mortality outcomes, the value of a statistical life and the amount of e-waste processed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.312
Teacher spread0.281 · 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