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Record W6981715033

E-waste transboundary movement regulations in various jurisdiction

2022· article· en· W6981715033 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsJurisdictionLegislationLiabilityTreatyState (computer science)Member stateStakeholderConformity assessment
DOInot available

Abstract

fetched live from OpenAlex

The growing volumes of electrical and electronic waste (e-waste), alongside their potential to be valuable resource or harmful pollutants, have made this waste stream an important subject of research, legislation, and policymaking. E-waste management comprises a series of activities, some of which are profitable and some of which are not. The current regulations in place exist in an attempt to ensure the unprofitable (but necessary) activities are undertaken in a sound manner (safe to humans and to the environment) and to state whom the stakeholder responsible for such activities are. The responsibility and financial liability may fall under the manufacturer (polluter-pay principle), the government, the consumer, or a combination of different stakeholders, and this varies widely from jurisdiction to jurisdiction. There are currently a set of international, national, and regional legislations/treaties that oversee the movement and management of e-waste. The most notorious international treaty is the Basel Convention, which is 30 years old, ineffective and contains known loopholes that allow disguised e-waste export as equipment for repair. In the national and regional scope, legislation concerning e-waste can vary widely, even within a single country – like in the case of China, Canada, and the USA. The lack of unity in legislation, or at least a set of regulations design to be collaborative with each other, is one of the current challenges worldwide. The use of conformity verification systems (e.g. WEEELABEX), a standardized universal extended producer responsibility policy or a significant update on the (outdated) international treaties, may be a solution, but it seems that the world is far from reaching these milestones. Finally, China's recent ban has affected the global WEEE market, which, in turn, has affected the regulatory framework of other Southeast Asian nations. More changes are expected in the near future due to the cascading effect of these changes and further import bans already announced by China.

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)0.013
metaresearch head score (Gemma)0.016
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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.016
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0030.005
Scholarly communication0.0080.006
Open science0.0020.006
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0120.004

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 head0.293
Teacher spread0.263 · 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
Published2022
Admission routes1
Has abstractyes

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