Bibliographic record
Abstract
In this paper two key costs of AD protection are documented. First, once AD has been adopted, countries often have a difficult time restraining its use. In recent years \′new' users have accounted for half of the overall world total. Many of the heaviest AD users are countries who did not even have an AD statute a decade ago. Second, I will show that that, on average, AD duties cause the value of imports to fall by 30–50 per cent. I find that trade falls by almost as much for settled cases as for those that result in duties. I also find that, even for those cases that are rejected, imports fall. JEL Classification: F13 A propos de la généralisation et de l'impact des mesures anti‐dumping. Ce mémoire souligne deux coûts importants des mesures de protection anti‐dumping (AD). D'abord, une fois la mesure en place, les pays ont souvent de grandes difficultés à en restreindre l'usage. Au cours des années récentes, les “nouveaux” utilisateurs de ces mesures comptent pour la moitié de l'activité AD dans le monde. Et plusieurs des pays qui en font un usage intensif n'avaient pas de loi AD il y a une décennie. Ensuite, en moyenne, les droits de douane AD entraînent une chute des importations de l'ordre de 30%à 50%. Et le commerce chute de presque autant pour les cas où il y a résolution du problème que pour ceux où un tarif de rétorsion est imposé. Fait intéressant, il appert que les importations chutent même dans les cas où la plainte est rejetée.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 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".