Bibliographic record
Abstract
For the purposes of the research for doctoral thesis, this article refers to the following 10 EU Member States, which cover the Central and Eastern Europe: Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Slovenia.The selection was made taking in consideration the appropriate similarities and differences in living standards, population, development orientations and EU Structural and Cohesion Funds allocations. Looking forward, it can be stated that this countries had an estimated share of available European funding, ranging from almost a tenth to approximately a quarter of their annual Gross Domestic Product. A comparison of the absorption rates during the 2007-2014 period can be critical towards reconsidering some theories of potential cohesion impact and reflecting them in current Eurostat statistics. It may be useful to acknowledge that most of the problems in contracting and disbursement of European funding occurred in every of the 10 Member States, nevertheless it cannot be held an underestimation of absorption capacities between the top and the tail countries, whereas some of the related countries (i.e. the 2004 EU accession countries) had more time and some experience in adjusting wrong or insufficient focus on absorption capacity. It is a well known fact that contracting of European Structural and Cohesion Funds started only in 2008 and reached a reasonable level in 2009, while payments tended to lag by a further year. By the end of 2014 most of the 10 Member States had the performance of reaching or overtaking the full contracting ratio, but payment ratios were situated much lower.The absorption process lies in the responsibility of each Member State. According to Hapenciuc et al (2013), the European Comission grants funds, proportional to each state’s GDP, but the institutional system, the procedures, the capacity of the beneficiaries to contract the necessary co-financing are all elements in which national authorities have a decisive role.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".