Currency Manipulation and WTO Laws: Should the Anti-Dumping Mechanism Be Entirely Dumped?
Bibliographic record
Abstract
Currency devaluation resembles subsidy and dumping in terms of its impact on global trade – it grants price advantages to exporting companies. Unlike subsidy and dumping, however, multilateral regulation of currency manipulation, which is principally exercised by the IMF, is far from sufficient. Meanwhile, the WTO, whose core principles are undermined by currency manipulation, plays no role in the regulatory framework. Against this background, this paper discusses possible avenues for the WTO to combat currency manipulation in future negotiations. Particularly, it proposes a new approach, which is to allow the application of the surrogate price method in anti-dumping investigations against currency manipulators. The anti-dumping mechanism has long been overlooked in the relevant literature as it is believed to combat company-level activities rather than state-level activities. By proposing the new approach, this paper does not argue to identify currency manipulation as a dumping activity. Rather, it proposes to treat fundamental exchange rate misalignment as a special market condition which allows anti-dumping investigation authorities to use the surrogate price method to eliminate the trade distortion caused by it.
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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.014 | 0.010 |
| 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".