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Record W2402688989 · doi:10.1111/sjtg.12149

A crack in the facade? Situating Singapore in global flows of electronic waste

2016· article· en· W2402688989 on OpenAlexafffund
Josh Lepawsky, Creighton Connolly

Bibliographic record

VenueSingapore Journal of Tropical Geography · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of CanadaEuropean Commission
KeywordsConventionFacilitatorState (computer science)HarmLawSociologyBusinessLaw and economicsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Singapore is alleged to be a key node in global flows of e-waste prohibited under the Basel Convention. We combine a close reading of the Convention and related documents with findings from nonparticipant observation of and interviews with Singapore-based traders of discarded electronics. The case offers both important conceptual and empirical findings for future studies of territory in market-making activity. Conceptually, our research suggests that it may be analytically useful in such studies to conceptualize territory without presupposing that it is generated as a result of separate domains or logics such as 'the political' or 'the economic'. Empirically, we find that the regulatory framework of the Convention, combined with the action of traders based in Singapore, generates a territorialization of the city-state such that it operates as a crack in the regulatory edifice of the Convention, even as Singapore lawfully fulfils its obligations to it. Moreover, allegations premised on the role of Singapore as a facilitator of global e-waste dumping misrepresent its crucial role as a conduit of electronic equipment for the significant reuse markets elsewhere in Southeast Asia and beyond. The case indicates that the allegations against Singapore hinge on the city-state being territorialized as a 'developing country'.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.345

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations8
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueSingapore Journal of Tropical GeographySame topicRecycling and Waste Management TechniquesFrench-language works237,207