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Record W1860121323 · doi:10.4018/ijfbmbm.2016070102

Processed Food Trade of Greece with EU and Non-EU Countries

2016· article· en· W1860121323 on OpenAlexaff
Pascal L. Ghazalian

Bibliographic record

VenueInternational Journal of Food and Beverage Manufacturing and Business Models · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsEuropean unionInternational economicsInternational tradeCompetition (biology)Gravity model of tradeEu countriesEconomicsDistribution (mathematics)Intra-industry tradeTrade barrierTrade diversionBusinessInternational free trade agreement

Abstract

fetched live from OpenAlex

This paper examines the implications of the European Union (EU) regional trade preferences for processed food trade between Greece and its EU partners, and between Greece and non-EU countries. The empirical analysis relies on the gravity model, and uses different estimation techniques. The results show that the EU regional trade preferences led to substantial increases in processed food trade between Greece and its EU partners, emphasizing trade creation effects. The magnitudes of these increases are higher than the intra-EU average, and are more pronounced for Greece's imports than for Greece's exports. The results also indicate that the EU regional trade preferences brought about decreases in processed food trade between Greece and non-EU countries, implying trade diversion effects. The Greek food processing industry could benefit from competitiveness-promoting strategies (e.g., upgrading innovation activities, marketing and distribution channels, and production efficiency) to expand exports to the EU market and to counter import competition in the domestic market. JEL Classification: F13, F14, F15.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.186
Teacher spread0.158 · 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 designObservational
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

Citations1
Published2016
Admission routes1
Has abstractyes

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