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Record W86842000 · doi:10.29173/alr297

The Many Dimensions of Softwood Lumber

2007· article· en· W86842000 on OpenAlexvenueaboutno aff
Jeffrey L. Dunoff

Bibliographic record

VenueAlberta Law Review · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsSoftwoodLegalizationInternational trade lawInternational tradePolitical scienceEconomicsLawEngineeringPulp and paper industry

Abstract

fetched live from OpenAlex

The Softwood Lumber dispute between Canada and the United States is one of the longest and most expensive trade disputes in history. However, the Softwood Lumber dispute has been, if not misunderstood, at least underappreciated. To date, the dispute has attracted attention because of the substantial economic interests involved, the complexity and length of the litigation, and the doctrinal implications of the various decisions rendered in domestic and international proceedings. This paper seeks to demonstrate that Softwood Lumber's central importance lies elsewhere; for trade scholars, Softwood Lumber is of interest because it exposes three of the central challenges facing the international trade regime: the potential displacement of an international regime by a spaghetti bowl of regional and bilateral treaties; the status of international trade norms in domestic courts; and the problem of selective and halting compliance by powerful states. But these challenges are, in turn, instantiations of three central challenges facing the field of public international law, namely the fragmentation of international law; the relationships among proliferating transnational courts; and the limits of (international) legalization. Thus, the systemic issues raised by Softwood Lumberprovide a tour d’horizon of debates central to contemporary international trade law and public international law.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.001

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 head0.016
GPT teacher head0.254
Teacher spread0.238 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2007
Admission routes2
Has abstractyes

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