PREGLED FUNK CIONALNIH REGIJ PO IZBRANIH DRŽAVAH REVIEW OF FUNCTIONAL REGIONS IN SELECTED COUNTRIES
Bibliographic record
Abstract
V prispevku je podan pregled funkcionalnih regij po izbranih državah. V ta namen smo najprej opisali osnovne koncepte opredelitve in členitve območij držav na funkcionalne regije. Sledi predstavitev funkcionalnih regij v izbranih petnajstih državah Evropske unije, to je v Avstriji, Belgiji, na Češkem, Danskem, Finskem, v Franciji, Italiji, na Madžarskem, v Nemčiji, na Norveškem, Poljskem, Portugalskem, v Španiji, na Švedskem, v Veliki Britaniji, ter dodatno v Švici, Kanadi in Združenih državah Amerike. Na koncu podamo še krajši pregled dosedanjih raziskav, opredeljevanja in razmejevanja funkcionalnih regij v Sloveniji glede na mednarodno primerjalno analizo ; In this article, a review of functional regions by selected countries is presented. For this purpose, the basic concepts of definition and delimitation of functional regions are presented, followed by a presentation of functional regions in the fifteen selected countries in European Union: Austria, Belgium, Czech Republic, Denmark, Finland, France, Italy, Hungary, Germany, Norway, Poland, Portugal, Spain, Sweden, Great Britain, as well as in Switzerland, Canada and United States of America. At the end, a short review of definition and delimitation of functional regions in Slovenia is presented from the international perspective.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".