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Impact of Environmental Policies in the Hog‐Pork Sector on Trade and Market Welfare in North America

2003· article· en· W2039117350 on OpenAlexfundvenueaboutno aff
Marielle Savard

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsnot available
FundersUniversité Laval
KeywordsWelfareFree tradeLiberalizationEconomicsPaymentAgricultureInternational economicsInternational tradeGovernment (linguistics)BusinessMarket economyEcologyBiology

Abstract

fetched live from OpenAlex

The objective of this study is to design a framework to assess trade patterns and market welfare (Marshallian measures of producer and consumer surpluses plus government payments) under various combinations of agricultural, environmental and trade policies. To reach this objective, an environmental model, EPIC, is linked to a model of the North American hog‐pork sector. The reduction in Québec inventories, following the implementation of environmental policies, triggers a decrease of Canadian live hog exports to the U.S. and an increase of U.S. pork exports to Canada. Environmental policies are responsible for decreases in welfare. The decrease in welfare is the largest when a moratorium in North America is simulated. Since trade liberalization has a positive impact on welfare, the welfare decrease from a moratorium is somewhat attenuated under free trade conditions.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.164
Teacher spread0.155 · 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 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

Citations0
Published2003
Admission routes3
Has abstractyes

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