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Record W3165559298

Trade Measures for Regulating Transboundary Movement of Electronic Waste

2018· article· en· W3165559298 on OpenAlexaff
Gideon Christian

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDeveloping countryInternational tradeBusinessCertificationElectronic wasteDumpingElectronic equipmentIndustrial organizationEconomicsEngineeringEconomic growthWaste management
DOInot available

Abstract

fetched live from OpenAlex

International trade in used electrical and electronics equipment (UEEE) provides an avenue for socio-economic development in the developing world and also serves as a conduit for transboundary dumping of waste electrical and electronic equipment (WEEE) also referred to as electronic waste or e-waste. The latter problem arises from the absence of a regulatory framework for differentiating between functional UEEE and junk e-waste. This has resulted in both functional UEEE and junk e-waste being concurrently shipped to developing countries under the guise of international trade in used electronics. Dealing with these problems will require effective regulation of international trade in UEEE from both exporting and importing countries. Although, the export of e-waste from the European Community to developing countries is currently prohibited, significant amount of e-waste from the region continue to flow into developing countries due to lax regulatory measures in the latter. Hence, there is need for a regulatory regime in developing countries to complement the prohibitory regime in the major e-waste source countries. This paper proposes trade measures modelled in line with WTO rules which could be adopted by developing countries in addressing these problems. The proposed measures include the development of a compulsory certification and labelling system for functional UEEE as well as trade ban on commercial importation of UEEE not complying with the said certification and labelling system. The paper then goes further to examine these proposed measures in the light of WTO rules and jurisprudence.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.007
Scholarly communication0.0050.004
Open science0.0020.003
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.009
GPT teacher head0.237
Teacher spread0.229 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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