MétaCan
Menu
Back to cohort
Record W7018966656

The European Union preferential trade with developing countries. Total trade restrictiveness and the case of sugar

2007· book-chapter· en· W7018966656 on OpenAlexaboutno aff

Bibliographic record

VenueIris (Roma Tre University) · 2007
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsDeveloping countryRestrictivenessGravity model of tradeEuropean unionTrade barrierComparative advantageTrade diversionOrder (exchange)Free tradeTrade creation
DOInot available

Abstract

fetched live from OpenAlex

In order to assess the extent to which developing countries receive preferential treatment from the EU, Conforti, Ford, Hallam, Rapsomanikis and Salvatici use a mercantilist trade restrictiveness index (MTRI). They demonstrate that although Least Developed Countries (LDCs) face a relatively low level of protection across all sectors, even before the implementation of the EU’s Everything but Arms (EBA) initiative, many developing countries are highly constrained in their trade with the EU. These developing countries include some of the more competitive developing countries such as Brazil and Argentina, but also the African, Caribbean and Pacific (ACP) non-LDC group. In agricultural trade, LDCs do not appear to be provided a high degree of preference by the EU, as they seem to face higher MTRI indices than other more developed countries such as Chile and Canada. Taking the issue of ACP non - LDC countries further, the paper investigates in more detail trade in sugar, a product where these countries face high protection at the margin. By using a global partial equilibrium model for the sugar market and a gravity model to replicate LDCs bilateral trade with the EU, to simulate EU sugar market reform proposals, they suggest that trade will be diverted from countries currently enjoying preferential access, particularly higher cost ACP countries exporting within the sugar protocol, and will be displaced by more efficient LDC producers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.040
GPT teacher head0.176
Teacher spread0.136 · 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 designTheoretical or conceptual
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

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueIris (Roma Tre University)Same topicGlobal trade and economicsFrench-language works237,207