How Do the "GATS-Plus" and "GATS-Minus" Characteristics of Regional Service Agreements Affect Trade in Services?
Bibliographic record
Abstract
Preferential liberalization of trade in services is a central feature of the new regionalism."GATS-Plus" and "GATS-Minus" have become the distinctive characteristics of the service RTAs and this paper aims to investigate and distinguish the different effect of the "GATS-Plus" and "GATS-Minus" components of RTAs on the service trade .The results of the empirical research by using the gravity equation either with time-varying exporter and importer fixed effects or with the specific exporter and importer fixed effect and year fixed effect both indicate : (1) belonging to a RTA (both "only goods" RTA and "service" RTA) can increase the bilateral service trade between the trading-pairs significantly.(2) almost all the "GATS-plus" and "GATS-neutral" commitments either on market access or on national treatment made by trading-pairs with each other under service RTAs have significantly positive effect on bilateral service export.(3) the commitments of "GATS-minus" characteristic do not have significant negative effects on bilateral service export because "GATS-minus" treatment can be neutralized to some extent by two main preferential erosion mechanisms under the RTAs: "liberal rule of origin" and "non-party MFN provision".
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".