Някои проблеми при адаптирането на България към общата търговска политика на ЕС
Bibliographic record
Abstract
България може да реализира с по-голям успех своите икономически и търговски цели, ако използва по-добре инструментите на Общата търговска политика на ЕС. За да постигне това, България трябва да промени стоковата структура на своя експорт и да я направи по-близка до стоковата структура на експорта на ЕС. В момента между тях има съществени различия. В експорта на България доминират материалоинтензивни и трудовоинтензивни стоки, а в експорта на ЕС – капиталоинтензивни и наукоинтензивни. Съществени различия съществуват също и по отношение на географското разпределение на търговията. Важни търговски партньори на ЕС като САЩ, Китай, Япония, Бразилия, Канада, играят твърде ограничена роля във външната търговия на България. Съществува и неизползван капацитет за развитие на търговията между България и редица африкански страни, които са поддържали с нея приятелски отношения в миналото и които сега ползват специален преференциален митнически режим в търговията си с ЕС. Abstract Bulgaria could achieve its economic and trade goals with a greater success by better using the tools of the EU Common trade policy. To do that Bulgaria has to change its exports commodity structure and to make it similar to the commodity structure of EU exports. At present time there are significant differences between these both structures. In Bulgaria’s exports dominate resources intensive and labor intensive goods and in EU exports dominate capital intensive and knowledge intensive goods. Significant differences exist also in the geographical distribution of trade. Important EU trade partners as USA, China, Japan, Brazil, Canada, play a quite limited role in Bulgaria’s foreign trade. There is also an unused capacity for development of the trade between Bulgaria and a number of African countries, which used to be in friendly relationship with Bulgaria in the recent past and are now under a special preferential tariff regime in their trade with EU.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.006 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.052 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.097 | 0.071 |
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; both teacher heads agree on what is shown here.
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".