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Record W2890369009

The Commonwealth advantage: trading with the bloc offers buoyant economic prospects

2018· article· en· W2890369009 on OpenAlexaboutno aff
Ruth Lea

Bibliographic record

VenueLondon School of Economics and Political Science Research Online (London School of Economics and Political Science) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
Fundersnot available
KeywordsCommonwealthBrexitInternational tradeNegotiationInternational economicsEconomicsTrade diversionEuropean unionFree tradePolitical scienceInternational free trade agreementLaw
DOInot available

Abstract

fetched live from OpenAlex

One of Brexit's potential advantages is the UK's freedom to negotiate its own trade deals instead of being dependent on the EU. Of course, trade will continue with the EU after Brexit, probably little changed, and there is little doubt that the EU will continue to be a major trading partner after Brexit. But it is widely expected that the share of UK exports going to the EU will continue to decline, reflecting the maturity of the EU markets and the continuing decline of the EU’s share of global output. New deals are likely to be with countries as diverse as the US and Japan – and, of course, individual Commonwealth countries. Australia and Canada, for example, have already expressed interest in a free trade agreement. In this post, Ruth Lea, CBE (Arbuthnot Banking Group), explains why trading with the Commonwealth offers buoyant economic prospects.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0130.008
Open science0.0000.005
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0190.003

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 head0.039
GPT teacher head0.367
Teacher spread0.328 · 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 designNot applicable
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

Citations2
Published2018
Admission routes1
Has abstractyes

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