Analysis on the Development and Influence of Overlapping Free Trade Agreement
Bibliographic record
Abstract
Since the 1990s, FTAs（FTA）have developed rapidly, and overlapping free trade agreement comes into being naturally. The overlapping free trade agreement causes multiple duplicate of rules of origin as well as reduces the economic efficiency of the operation of FTAs; industries in the area bring about cohesive effect in hub country, and specialization effect of decentralization in spoke countries; the phenomenon of overlapping free trade agreement complicates the relationship between FTA and multilateral trade system (MTS), making the occurrence of transitional institutional arrangement possible in the process of upgrading from FTA to Customs Union. Key words: Overlapping; Free Trade Agreement; Rules of Origin; Customs Union FTA Resume: Depuis 1990, FTAs（ FTA） s’est developpee rapidement et le chevauchement des accords de libre-echange apparaissent. Le chevauchement des accords de libre-echange genere de multiples duplicata de regles d’origine ainsi que l’affaiblissement de l’efficacite economique des operations de FTAs; les industries dans la region engendrent des effets cohesifs dans les pays-centres et des effets de specialisation a cause de decentralisation dans les pays-satellite; le phenomene du chevauchement des accords de libre-echange complique les relations entre FTA et le systeme d’echange multilateral (MTS) en rendant possible l’occurrence des arrangements institutionnels traditionnels dans le processus d’actualisation de FTA a la union douaniere. Mots-Cles: chevauchement; accord de libre-echange; regles d’origine; union douaniere; FTA
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".