Bibliographic record
Abstract
Abstract Exchange rate manipulation—the active devaluation of a currency through intervention in the foreign exchange market—is a frequent trigger of international disputes. Yet it is not an obvious policy choice: as a blunt tool to boost export competitiveness, it is disliked by citizens and importers because of the loss of purchasing power it entails, and because it benefits those with investment abroad at the expense of those with savings at home. It is thus notable that a group of East Asian countries, from Japan and Korea to Thailand, undertake frequent and often large interventions to devalue their currencies. What explains their policy choice? We provide evidence that exchange rate depreciations are undertaken at the behest of export industries. Because lobbying activities in East Asian countries are not directly observable, we focus on Japan and Korea and construct a proxy measure of lobbying by exporters, drawing on news reports. We use machine learning to scale daily reports of industry demands in the two leading financial newspapers, the Japanese Nihon Keizai Shimbun and, in a robustness check, the Korean Hankyung, over twenty-five years. We find evidence that mounting public pressure by organized economic interest groups precedes intervention and induces currency depreciation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".