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Record W7070914475

Quantifying the economic impact of CETA on Portugal

2017· dissertation· en· W7070914475 on OpenAlexaboutno aff

Bibliographic record

VenueRepositório do ISCTE-IUL · 2017
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic impact analysisWineRevenueEuropean unionProduction (economics)ProductivityPortugueseValue (mathematics)Agriculture
DOInot available

Abstract

fetched live from OpenAlex

The European Union and Canada recently signed a Comprehensive Economic Trade Agreement (CETA) which covers virtually all sectors and aspects of Canada-EU trade. For small open economies, like Portugal, trade is a key factor in productivity and competitiveness gains that foster economic performance and growth. Canada is among Portugal's top 10 importing countries, and wine is Portugal's main export good to Canada. The wine sector represents an important source of economic revenue for Portugal, contributing very significantly to the final value of agricultural production and exports. This dissertation studies the impact of CETA on Portugal-Canada wine trade, assessing its effects on the Portuguese wine sector. After a thorough examination of the Canadian wine sector, the Canadian consumer profile and market trends, an analysis was made to address the strengths, weaknesses, opportunities and threats related to Portugal's positioning in the Canadian market in view of CETA's entry into force.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.621
Threshold uncertainty score0.762

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.016
GPT teacher head0.340
Teacher spread0.324 · 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
Published2017
Admission routes1
Has abstractyes

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