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Record W2155680804 · doi:10.1139/x09-026

An economic perspective on the determination of dumping in the US–Canada softwood lumber trade — an analysis for Ontario

2009· article· en· W2155680804 on OpenAlexafffundvenueabout
Feng’e Yang, Shashi Kant, Emmanuel Asinas

Bibliographic record

VenueCanadian Journal of Forest Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsMinistry of Economic Development, Job Creation and TradeUniversity of Toronto
FundersMinistry of Natural Resources
KeywordsSoftwoodAgricultural economicsProduct (mathematics)EconomicsInternational tradePulp and paper industryEngineeringMathematics

Abstract

fetched live from OpenAlex

Dumping has been one of the most controversial issues in the never-ending softwood lumber trade dispute between the United States (US) and Canada. In this paper, we investigate whether the softwood lumber producers in Ontario dumped product into their major market in the US during the period from April 1996 to September 2006. The Enhanced Parity Bounds Model was used to explore the possibility that Ontario’s softwood lumber producers had exercised price discrimination between the Toronto market and the Great Lakes market. Our analysis indicates that the industry had on average charged a higher price in the Great Lakes market than in the Toronto market during this period. Based on this evidence and the economic conditions in which a US antidumping investigation and two administrative reviews were conducted, we draw the conclusion that the Ontario’s softwood lumber producers did not dump product into the US market during the study period.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.035
GPT teacher head0.315
Teacher spread0.279 · 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

Citations1
Published2009
Admission routes4
Has abstractyes

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