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Record W2033679239 · doi:10.5539/ijef.v5n5p131

Trade Creation and Trade Diversion between Tunisia and EU: Analysis by Gravity Model

2013· article· en· W2033679239 on OpenAlexvenueno aff
Ahmed Zidi, Said Miloud Dhifallah

Bibliographic record

VenueInternational Journal of Economics and Finance · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsTrade diversionTrade creationGravity model of tradeInternational tradeEconomic integrationInternational economicsBilateral tradeEconomicsInternational free trade agreementTrade barrierFree tradeBusinessGeographyChina

Abstract

fetched live from OpenAlex

Since the middle of nineties, there has been a great rise of free trade agreements (FTAs) between the North and South countries. Indeed, the objective of this article is to know if FTASbetween an ndustrialized region as Europe and a small country as Tunisia are capable of increasing exchanges among them and then improving the trade of the weakest country. Our aim is to know if agreements between industrial countries and developed countries are able to increase trade between them and therefore improve the trade of the less developed country. To answer to this question we evaluate the two effects of regional integration: trade creation and diversion trade. We obtain two main results: the first result is after five years of the agreement between Tunisia and Europe, there is no trade creation. The second result shows that the preferential agreement between the two partners does not generate trade diversion of imports. However, there is a trade diversion of exports.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.031
GPT teacher head0.212
Teacher spread0.181 · 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 designSimulation or modeling
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

Citations19
Published2013
Admission routes1
Has abstractyes

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