Bibliographic record
Abstract
But sufficient justification is to be found in the usage of the most authoritative writers and in the considerations of economy and precision of terminology for confining the term dumping toprice‐discrimination between national markets(original emphasis). Jacob Viner (1923, p. 3) 1 Anti‐dumping cases are on the rise internationally and are becoming a major weapon in the protectionist arsenal. The definitions of dumping, however, are not based on theoretically sound economic reasoning meaning firms are often falsely accused of engaging in unfair pricing practices when exporting. The historic bases of the current definitions are outlined and their theoretical deficiencies fully explained. Suggestions are made regarding how the definition of dumping used in international law could be put on a sound economic basis. Les cas d'antidumping sont internationalement à la hausse et deviennent une arme importante dans l'arsenal du protectionniste.Cependant, les définitions de dumping ne sont pas basées sur un raisonnement économique théoriquement sains, c'est à dire les firmes exportatrices sont souvent faussement accusées de s'engager dans des pratiques de tarification favorables. Les bases historiques des définitions courantes sont décrites et leurs insuffisances théoriques sont entièrement expliquées. Des suggestions sont faites concernant la façon dont la définition de dumping dans le droit international pourrait être basée sur des concepts économiques solides.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".