Concluding Remarks by the Chairperson of the Trade Policy Review Body, H.E. Mr. Ángel Villalobos Rodríguez of Mexico, at the Trade Policy Review of New Zealand, 1 and 3 June 2022.
Bibliographic record
Abstract
The sixth Trade Policy Review of New Zealand has offered us a good opportunity to deepen our understanding of recent developments in, and challenges to, its trade, economic, and investment policies since its fifth TPR in 2015. I would like to thank the New Zealand delegation, led by H.E. Ambassador Clare Kelly, Permanent Representative of New Zealand to the WTO, for the active participation in this exercise. My gratitude also goes to our discussant H.E. Ambassador Stephen de Boer, Permanent Representative of Canada, for his insightful comments and to the 41 delegations that took the floor during this meeting. The strong interest in New Zealand’s trade policies is also evident from the 442 advance written questions, 268 sent before the meeting.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.034 | 0.026 |
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".