Bringing Agriculture into the GATT
Bibliographic record
Abstract
this paper, under the Chairmanship of Stefan Tangermann. Each co-author contributed a draft of one or more chapters and participated in reading and improving the other chapters. The drafting responsibilities, reflecting geographical experience, were as follows: chapter 2 (USA) Sumner; chapter 3 (EU) Tangermann; chapter 4 (Canada) Miner and McClatchy; chapter 5 (Australia and New Zealand) MacLaren; chapter 6 (Japan) Honma; chapter 7 (Korea) Lee; chapter 8 (South Asia) Pursell; chapter 9 (Latin America) Valdes. Tangermann drafted chapters 1 and 10, Josling chapter 11, and Miner chapter 12. The authors are aware of the fact that different views on a number of policy issues are occasionally presented in the individual country chapters. To a large extent, these divergencies of views reflect different attitudes adopted in the respective countries, and it is for that reason that not all of them have been ironed out in the process of editing the country drafts. Though not necessarily agreeing with every sentence in the paper, each of the authors has nevertheless agreed to be associated with the entire report
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".