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Record W1787918301 · doi:10.1111/roie.12143

Intra‐industry Trade Liberalization and the Environment

2014· article· en· W1787918301 on OpenAlex

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

VenueReview of International Economics · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsThompson Rivers UniversityUniversity of Manitoba
Fundersnot available
KeywordsMonopolistic competitionEconomicsFree tradeInternational economicsLiberalizationProduction (economics)International tradePollutionCompetition (biology)Product differentiationMarket economyMonopolyMacroeconomics

Abstract

fetched live from OpenAlex

This paper examines how trade liberalization affects national and global pollution in a multi‐country model incorporating monopolistic competition and intra‐industry trade as well as inter‐industry trade. Each country produces skill‐intensive differentiated goods and labor‐intensive goods. Pollution is a by‐product of production but pollution abatement can be undertaken. Regardless of country characteristics, if the differentiated‐good sector is sufficiently cleaner (dirtier) then, without any change in environmental taxes, a multilateral reduction in import protection accorded to the differentiated good or to both goods typically leads to a decline (rise) in pollution in all countries. Pollution havens tend not to arise.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.457

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 studies0.0000.000
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 head0.025
GPT teacher head0.202
Teacher spread0.177 · 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