The Profile of Local Political Elite and Strategy Prioritisation at the Local Level in ECE Countries. Case Studies: Tecuci (Romania), Českà Lípa (the Czech Republic), Oleśnica (Poland) and Gyula (Hungary)
Bibliographic record
Abstract
The paper constitutes an attempt at examining the outlook and strategies of the local political elites in ECE countries. Specifically, the inquiry employs four elite groups in four towns, demographically similar and in developmental strategies: Tecuci (Galați county, Romania), Česká Lípa (Liberec region, the Czech Republic), Oleśnica (Lower Silesia voievodship, Poland), and Gyula (Békés county, Hungary), through the prism of the members of the Municipal Councils, for identifying and analyzing (a) the values, (b) the interactions with other groups, networks of power at local level, and (c) the priorities of the local elites. The level of decentralization specific for each of the four countries is employed as the major explanatory trajectory for the differences encountered; subsidiarily, the explanation focuses also on legacies of the ancien régime, experienced locally. Three models of local leadership in ECE are proposed: “predominantly elitistic”, “democratic elitist”, and “predominantly democratic”, with the prospects of verifying their validity and pertinence in similar cases throughout the region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".