Identifying strategic development objectives for European Union states using the Dominance-based Rough Set Approach: the case of Poland
Bibliographic record
Abstract
The use of the dominance-based rough set approach (DRSA) to help identify and prioritize strategic political, economic, sociological and technological (PEST) objectives for European Union (EU) countries is presented. The countries are first grouped into three categories: [A] those that are doing well according to the selected indicators; [B] those that need support to acquire category A status; [C] those ranked the lowest and needing special support with regard to the criteria considered. The categories correspond to tertiles within the average ranking of all EU countries. DRSA then provides decision rules based on PEST needs in order to improve the development and classification of the country. We conclude that by using this methodology, the EU could identify the strategic objectives to be given priority in order to stimulate its economic development or to improve the economic and sociological status of any country in the union. The case of Poland, a category C country from an economic perspective, is of particular interest.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".