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Record W2121540661 · doi:10.3386/w10168

Competitive Liberalization and a US-SACU FTA

2003· report· en· W2121540661 on OpenAlexaff
John Whalley, J. Clark Leith

Bibliographic record

VenueNational Bureau of Economic Research · 2003
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsWestern University
Fundersnot available
KeywordsLiberalizationInternational tradeCustoms unionInternational economicsBusinessEconomics

Abstract

fetched live from OpenAlex

This paper evaluates a possible US-SACU (Southern African Customs Union) free trade agreement as part of a US approach to new preferential trade agreements characterized by the term "competitive liberalization."This is the idea that competition among large countries (US/EU) to negotiate preferential arrangements with smaller countries or regions will lower barriers, and eventually add fresh impulse to new multilateral WTO negotiations.In commercial policy terms, the US interest in such an arrangement lies in improved access to a smaller but more protected market where the EU already has preferential arrangements, and the SACU interest lies in improved access to a much larger but less protected market.There is also a SACU interest in weakening the trade restrictive effects of MFA quotas in the US for apparel imports.The risk of entrapment in extremely complex rules of origin arrangements which at times close markets (as in NAFTA and other US bilaterals) is a concern for SACU.Also, gains to SACU may be only temporary because of the US proposal to eliminate non agricultural tariffs entirely in the WTO by 2015.In key non commodity trade areas (services, investment, intellectual property, temporary entry of business persons), if other US bilaterals are any guide most liberalization requested will be heavily asymmetric if not unilateral on the SACU side.SACU does not currently cover any of these items since it is only a customs union, and prior negotiation will be needed among SACU countries.SACU also clearly has an interest in coordinating its negotiation with other US bilateral negotiating partners.These and other barriers to negotiation (including negotiating capacity constraints in several SACU members) will influence the outcome of negotiations.

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)0.006
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.035
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0040.003
Scholarly communication0.0090.005
Open science0.0010.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.472
GPT teacher head0.449
Teacher spread0.023 · 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
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

Citations12
Published2003
Admission routes1
Has abstractyes

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