Bibliographic record
Abstract
Dumping is whatever you can get the government to act against under the antidumping law. J. Michael Finger (1993, p.viii). Antidumping policy has become one of the most important instruments for protection in the international trade system, but at the same time it is the subject of an intense, though rather chaotic, debate. This paper provides an overview of the antidumping literature and the current issues. First it describes the origins of antidumping policy and provides some basic statistics on its current use drawn from several empirical studies. Next the paper discusses the economic foundations of antidumping law by examining the traditional and modern theories of dumping, as well as the industrial organization literature on price discrimination and predatory pricing. It is demonstrated that those economic foundations are weak. The paper also considers the fairness rationales for antidumping policy. Finally, it addresses the criticisms of antidumping laws, in particular in the context of the current antidumping versus competition policy debate, and discusses a variety of proposals for reform that have been made. The paper shows that the 1997 ‘cease fire’ agreement between Canada and Chile is a promising approach toward reform of antidumping policy.
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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".