Bibliographic record
Abstract
Several of the recently negotiated regional trade agreements contain significantly fewer concessions by the large countries to smaller countries than vice versa. In this paper, we compute post‐retaliation Nash tariffs by region under various regional trade arrangements using a calibrated numerical general equilibrium model of world trade. Regional agreements constrain strategic behaviour within each trading area, and (in the Customs Union case) enhance it outside the bloc. Results confirm the intuition that without side payments large‐small country regional agreements (such as the Canada‐U.S. agreement) would not have occurred. Le nouveau régionalisme: libéralisation du commerce ou assurance? Dans plusieurs des accords de libre échange régionaux négociés récemment, les grands pays accordent beaucoup moins de concessions aux petits pays que les petits aux grands. Dans cet article, les auteurs mesurent, à l'aide d'un modèle numérisé d'´equilibre général du commerce mondial, les droits de douane à la Nash après ajustements réciproques. Il semble que les accords régionaux contraignent les comportements stratégiques dans chaque bloc, et (dans le cas de l'union douanière) renforcent les comportements stratégiques hors du bloc. Ces résultats confirment l'intuition qui suggère que, sans ces arrangements parallèles, les accords entre grands et petits pays (comme l'accord U.S.A.‐Canada) ne se matérialiseraient pas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".