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Record W2094533675 · doi:10.2202/1524-5861.1156

Commentary: Free Trade Agreements and the Doha Development Agenda

2005· article· en· W2094533675 on OpenAlexaffabout
Dan Ciuriak

Bibliographic record

VenueGlobal economy journal · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsGlobal Affairs Canada
Fundersnot available
KeywordsFree tradeInternational economicsInternational tradeNegotiationRules of originLiberalizationInternational free trade agreementOrder (exchange)Trade barrierMargin (machine learning)Market accessEconomicsBusinessRegional tradePolitical scienceMarket economy

Abstract

fetched live from OpenAlex

Commentary on FTAs and the Doha Round Agenda. Dan Ciuriak is Deputy Chief Economist at Canada’s Department of International Trade. He is co-editor of, and regular contributor to, the Department’s annual Trade Policy Research series and advises on a wide variety of international economic issues, including WTO and NAFTA trade litigation. In his personal capacity he has published a number of articles on various aspects of economic globalization, with a particular focus on the Asian Crisis and China’s economic integration into the global economy. From 1994-1998, Ciuriak served as deputy to the Chair of the APEC Economic Committee with principal responsibility for editing the annual APEC Economic Outlook and other Economic Committee publications. From 1990-1994, he served as Finance Counsellor at Canada’s Embassy in Germany, covering G-7 issues, German reunification, the Maastricht process, and the European Monetary crisis. Previously, he was with Canada’s Department of Finance where he was deeply involved in Canada’s federal financial institutions reforms. He studied at McMaster University in Hamilton, Ontario.

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)0.012
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.095
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0120.011
Scholarly communication0.0100.011
Open science0.0050.004
Research integrity0.0730.060
Insufficient payload (model declined to judge)0.0090.003

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 head0.039
GPT teacher head0.209
Teacher spread0.170 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2005
Admission routes2
Has abstractyes

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