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Record W3186875561 · doi:10.1017/s1474745621000240

‘Two Roads Diverged in [Soft]wood’ Targeted Dumping, Differential Pricing Methodology, and Zeroing: <i>US – Canada Anti-Dumping in Softwood Lumber</i>

2021· article· en· W3186875561 on OpenAlexaffabout
Eugene Beaulieu, Janet Whittaker

Bibliographic record

VenueWorld Trade Review · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSoftwoodSubsidyAppealDifferential (mechanical device)DumpingEconomicsInternational tradeLawPolitical scienceEngineeringPulp and paper industryMarket economy

Abstract

fetched live from OpenAlex

Abstract The United States and Canada have a long-standing series of disputes over softwood lumber that until now have focused on alleged subsidies and countervailing duties (CVDs). The United States changed things up this time around and the US Department of Commerce (USDOC) found dumping after applying the Differential Pricing Methodology to softwood lumber from Canada. The panel found that the USDOC erroneously aggregated export price differences when applying the differential pricing methodology (DPM), but departed from the WTO Appellate Body's previous ruling in US–Washing Machines regarding the use of zeroing and the inclusion of differential prices under Article 2.4.2 of the Anti-Dumping Agreement. To date, the United States and Canada have not been able to resolve the long-standing softwood lumber dispute, and this time the focus shifts from subsidies and countervailing duties to anti-dumping duties. It remains to be seen what happens in this specific dispute on appeal – if, and when, the WTO Appellate Body starts to function again. It will also be interesting to see whether this panel decision encourages parties to argue for, and future panels to permit departures from, Appellate Body rulings with which they disagree.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.075
GPT teacher head0.252
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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
Published2021
Admission routes2
Has abstractyes

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