Bibliographic record
Abstract
Developing countries have a particularly hard time ahead in the negotiating agenda on the reform of international trade. In a sense they are committed to fight against the tide of free trade and the principles enshrined therein: most favored nation status, which means that the most favorable terms given to any party must be extended to all, and the rule on national treatment, which means that the concessions extended to locally produced goods and services must be extended to foreign counterparts. The apprehension that the undiluted application of these principles is a road to disaster has strengthened the search for exceptions; the danger is that this can put developing countries on the defensive, as can be gleaned from the very name of the exceptions: Special and Differential Treatment, Preference, Waivers. Thus, they may find themselves in the position of always having to make the case, or worse, of seeming eccentric, even perverse, especially in the light of the view that the application of free trade principles will increase aggregate welfare. How the jurisprudence of the WTO will develop as the issues become more and more technical, for example in the calculation of dumping margins, or injury, or in SPS cases, over the appropriate scientific evidence, is anybody's guess.
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 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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".