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Record W6964675056 · doi:10.2760/65213

EU Exports to the World: Effects on Income

2018· book· en· W6964675056 on OpenAlexaboutno aff

Bibliographic record

VenueJoint Research Centre (European Commission) · 2018
Typebook
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)European unionMember stateTrade barrierEuropean commissionMember statesProduction (economics)Commercial policyEconomic integration

Abstract

fetched live from OpenAlex

The European Commission identified trade policy as a core component of the European Union’s 2020 Strategy. The fast changing global economy, characterised by the dynamic creation of business opportunities and increasingly complex production chains, means that it is now even more important to fully understand how trade flows affect income generation. Gathering comprehensive, reliable and comparable information on this is crucial to support evidence-based policymaking.\n\nGuided by that objective, the European Commission’s Joint Research Centre (JRC) and the Commission’s Directorate General for Trade have collaborated to produce this publication. It aims to be a valuable tool for trade policymakers.\n\nFollowing up the first edition (Arto et al., 2015), the report features a series of indicators to illustrate in detail the relationship between trade and income (i.e. value added) generation for the EU as a whole and for each EU Member State using the World Input-Output\nDatabase (WIOD), 2016 release (Timmer et al., 2015, 2016), as the main data source. This information has been complemented with data on labour compensation by skill from EUKLEMS. All the indicators relate to the EU’s exports to the rest of the world so as to reflect the scope of EU trade policymaking.\n\nMost indicators are available as off 2000 but, due to data constraints, the indicator on labour compensation split by skill is only available from 2008 to 2014. The geographical breakdown of the data includes the 28 EU Member States, Australia, Brazil, Canada, China, India, Indonesia, Japan, Mexico, Norway, Russia, South Korea, Switzerland, Turkey, Taiwan, the United States of America, and an aggregate “Rest of the World” region. On the basis of the value added embodied in every million EUR worth of exports in 2014 and more recent data on international trade in goods and services, this report also provides projections elaborated by the JRC for 2017 using a different methodology, so they should be taken with caution.\n\nThe information presented in this pocketbook is complemented with an electronic version allowing downloads of the tables with the complete time series (2000-2014 and 2017).

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)0.003
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 score0.011
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.050
GPT teacher head0.304
Teacher spread0.254 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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