Identifying Strategic Development Objectives for European Union’s Potential Candidate States Using Dominance-Based Rough Set Approach: Case Study of Bosnia and Herzegovina
Bibliographic record
Abstract
This research is the second of a series of three researches to cope with strategic development objectives using Dominance-based Rough Set Approach (DRSA). The objective of this article is to expose the results of a research using DRSA to help the European Union (EU) identifying political, economical, sociological and technological strategic objectives for potential candidate countries planning to join the EU. Using the proposed methodology, politicians and leaders will be able to prioritize strategic development objectives according to political, economical, sociological and technological (PEST) needs of a specific candidate country to the EU. More precisely, the proposed methodology classifies all the European Union’s countries according to the following three different categories: [A] EU countries that are doing well according to the selected indicators; [B] EU countries that need support to acquire category A status; [C] EU countries ranked the lowest and needing special support with regard to the criterion or criteria considered. The three categories are delimited by tertiles relative to the average ranking of all EU countries including a potential candidate country, Bosnia and Herzegovina. Afterwards, DRSA provides decision rules based on this classification. These decision rules thus focus on the PEST needs of countries with respect to improve their development and classification by pointing out what was needed to be part of the different categories. We strongly believe that by targeting these identified needs, this research will help the development of the European Union’s economy, target and prioritize economical and sociological improvements with the use of strategic objectives for any candidate country. One of the results concerning our case study with Bosnia and Herzegovina is about the fact that this potential country has a weakness in the percentage of women in politics. Indeed, our research as shown that this criterion has an impact for the overall classification of the EU countries.
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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".