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Record W4386441394 · doi:10.1017/s1474745623000058

The Economic Interest Test in UK Trade Remedy Investigations

2023· article· en· W4386441394 on OpenAlexaboutno aff
Ilona Serwicka, G. P. Chapman, Bradley Tyler

Bibliographic record

VenueWorld Trade Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsnot available
Fundersnot available
KeywordsInternational tradeTest (biology)European unionEconomic integrationInternational economicsStakeholderTrade barrierTrade unionSingle marketBusinessEconomicsWorld tradePolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract The UK's Trade Remedies Authority (TRA) conducts economic assessments of the ramifications of trade remedies, the Economic Interest Test (EIT). Such assessments are not mandated by the World Trade Organization but are conducted by certain trade remedy investigating authorities, including those of Brazil, Canada, the European Union, and New Zealand. The EIT is a mandatory part of the UK trade remedy system and is arguably more transparent than similar interest tests conducted by other trade remedy investigating authorities. However, stakeholder participation remains a challenge and the TRA is working on ways to improve participation. To date, the TRA has completed 11 EITs in its trade remedy cases, with a further ten live cases. These cases cover different products, markets, and countries, across which the likely positive and negative impacts of trade remedy differ. This paper invites experts to review the TRA's EIT methodology.

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)0.095
metaresearch head score (Gemma)0.432
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 score0.095
Threshold uncertainty score0.504

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.432
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0120.011
Science and technology studies0.0020.011
Scholarly communication0.0120.009
Open science0.0020.007
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0140.002

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 head0.216
GPT teacher head0.272
Teacher spread0.055 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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