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Record W2785582458 · doi:10.15544/rd.2017.116

CETA AND ITALIAN AGRI-FOOD PRODUCTS: AN ANALYSIS ON COMPARED ADVANTAGES OF THE MAIN ITALIAN AGRI-FOOD SECTORS

2018· article· en· W2785582458 on OpenAlexaboutno aff
Daniele Bertolozzi-Caredio, Asta Raupelienė

Bibliographic record

VenueProccedings of International Scientific Conference "RURAL DEVELOPMENT 2017" · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsEuropean unionBusinessProduct (mathematics)Comparative advantageInternational tradeDisadvantageRevealed comparative advantageCompetitive advantageLiberalizationFree tradeAgricultural economicsInternational economicsCommerceEconomicsMarketingMarket economyComputer science

Abstract

fetched live from OpenAlex

At the age of second-generation agreements, the European Union is going to achieve a number of new trade deals, as well as others country, first of all the Comprehensive Economic and Trade Agreement treated with Canada. A significant part of the debating about CETA is focused on the real need or not to reach new deal and add more liberalization, in particular regarding the Agri-food goods. EU, and above all Italy, can boast a number of excellent export Agri-food processed product, such as wine, cheese and pasta; at the same time, Italy has a need of primary goods, like wheat. Revealed Competitive Advantage is an indicator of the importance of a specific product and, specifically, it’s used to identify the advantage or disadvantage of a trade flow. Some of the main Italian products exported in Canada have been analysed, just like the main imported product from Canada, the wheat; as opposed to EU-28 import of Durum wheat, the other trades have showed a comparative advantage in trade. Finally, in three cases, Italy proves greater advantages in respect with the EU.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.032
GPT teacher head0.244
Teacher spread0.212 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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