Céline Carrère* Jaime de Melo**
Bibliographic record
Abstract
The paper develops two synthetic measures at the HS-10 level to depict effective market access for a country receiving preferential access and applies these to the market access ASEAN members would receive on impact following the implementation of an FTA with the EU. These measures reveal quite a different picture than one that would be gleaned from the more usual ex-ante aggregate approaches. First, the measures show that current effective market access for ASEAN EBA members is cut in half by the preferences granted by the EU to countries that compete with these countries in the EU markets. Second, the small value of preferences is reflected in the pattern of preferential margins, the “significant ” preferential margins almost always being for products that account for less than 1/10 of 1 percent of exports at the HS-10 level. Third the measures show that about one quarter of the preferential margin under the proposed FTA for EBA members would be lost as a result of preferential access granted to ASEAN GSP members. Fifth, disaggregated calculations on the restrictiveness of rules of origin not only confirm that rules are more restrictive for products with higher preferential margins, but also that, for
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".