The Economic Interest Test in UK Trade Remedy Investigations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.095 | 0.432 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".