Preferencyjne porozumienia handlowe – znaczenie dla handlu dobrami i innych dziedzin współpracy Unii Europejskiej z partnerami zagranicznymi
Bibliographic record
Abstract
The EU’s trade in goods with the majority of its partners is regulated by preferential agreements, of a unilateral or mutual character, under PTAs. Today, the role of PTAs in eliminating tariff barriers is not important for the EU, mainly because, first, EU imports include many goods for which the MFN duty rate is 0%, and, second, the preferential margin (the difference between 0% preferential duty and MFN duty above 0%) is low in the EU. Also, all mutually preferential agreements, be they free trade agreements or customs unions, provide for some exceptions. The exceptions cover specific agricultural products the EU considers to be sensitive.Apart from the improved tariff access the EU gains to partners’ markets, a far more important objective for the EU in negotiating PTAs, is that there is a willingness to eliminate barriers of regulatory character, which have recently been the most important impediments for EU exporters. PTAs go beyond the existing WTO multilateral arrangements and are used by the EU to achieve foreign policy objectives, such as political and economic stabilisation in its vicinity and strengthening the role of the EU in the world.The EU has recently negotiated mutually preferential agreements with a number of neighbouring countries, under the European Neighbourhood Policy. It has also been negotiating agreements with key developed countries, including Canada, Japan, and the US. It has done so to deepen its ties with those partners’ producers and investors, and also to address the low efficiency of WTO multilateral rules which do not properly apply to particular aspects of concrete relations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".