関税を導入した国際貿易空間均衡モデルへの輸出補助金の導入と国際乳製品市場へのその適用 : 完全競争市場の場合
Bibliographic record
Abstract
Shono and Kawaguchi (1999a, 1999b, 2000), Kawaguchi and Shono (2000) present spalial equilibrium models of international trade under real tariff quota system with specific duties and ad valorem duties, and show how to integrate other international trade systems into those models. Shono (2000) applies those models to make quantitative analysis of effects of the WTO agreement on international trade of dairy products, especially focussing on effects of the change of tariff rates and current access quantities. This paper is the sequel of Shono (2000), and makes quantitalive analysis of effects of the change of export subsidies on international trade of dairy products, Following Shono (2000), we study about nonfat dry milk, butter, and cheese using ten-countries perfect competition model. Countries studied are Argentine (Arg.), Australia (Aus.), Brazil (Braz.), Canada (Can.), European Union (EU), Japan (Jap.), Mexico (Mex.), New Zealand (N.Z.), Poland (Pol.), and United States of America (USA). We throw light on effects of the change of export subsidies on international trade of the above mentioned dairy products. Implications of this paper and problems to be solved in the future are also summarised.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".