Export competition issues after Nairobi
Bibliographic record
Abstract
Developed countries shall immediately remove their existing export subsidies entitlements.However, Canada, the EU, Norway, and Switzerland can extend them for processed products, dairy, and swine meat up to 2020 provided they eliminate them for LDCs in 2016.Developing countries have until 2018 to remove their export subsidies and up to 2022 for products or groups of products for which a Member has notified export subsidies in one of its three latest export subsidy notifications. 2This period is defined as 18 months except for LDCs, NFIDCs, and a few additional developing countries for whom the maximum repayment term will be between 36 and 54 months, or an unlimited period in the case of Cuba.3 China has declared itself in a position to do so to the extent provided for in its preferential trade arrangements.12 See Lau, Schropp, and Sumner (2015), "The 2014 US Farm Bill and its Effects on the World Market for Cotton."ICTSD, Geneva.www.ictsd.org/node/9516913 See Glauber in this volume.For example, China has recently announced reforms for cotton, rapeseed and now corn.14 While this might not happen immediately for most Members, it is worth recalling that recently acceded Members, such as China or Russia, use more recent reference periods (1996-98 for China and 2006-08 for Russia) with current prices already below or close to such levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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; both teacher heads agree on what is shown here.
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".