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Record W2100088603 · doi:10.1162/15263800260047808

Environmental Change, Protest, and Havens of Environmental Degradation: Evidence from Asia

2002· article· en· W2100088603 on OpenAlexaff
Derek Hall

Bibliographic record

VenueGlobal Environmental Politics · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsEnvironmental degradationPoliticsInternational tradeEconomyEnvironmental politicsSoutheast asiaForeign direct investmentAsia pacificPolitical scienceDevelopment economicsEconomicsSociologyEcology

Abstract

fetched live from OpenAlex

This paper explores the relevance for the debate on “pollution havens” of two cases from the international political economy of Japan-Southeast Asia relations. It begins by suggesting that the typical focus of the pollution havens literature is too narrow, and concentrates instead on the broader question of the extent to which the environmental transformations associated with particular sectors influence their international siting patterns. The first case—the changes in Japanese FDI to Asia in the 1970s—demonstrates that Japanese firms and the Japanese state consciously attempted to relocate highly-polluting industry in order to escape anti-pollution protest in Japan. The second case—the effort to create in Asia and the Pacific an export-oriented industrial tree plantation (ITP) sector supplying regional pulp and paper markets—shows, somewhat counterintuitively, that political contestation related to the environmental problems caused by ITPs has encouraged Japanese companies to concentrate their tree planting activity not in Southeast Asia but in Australia.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.040
GPT teacher head0.198
Teacher spread0.158 · 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; both teacher heads agree on what is shown here.

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

Citations23
Published2002
Admission routes1
Has abstractyes

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