Evaluation of the Financial Performance of the Municipalities in Slovakia in the Context of Multidimensional Statistics
Bibliographic record
Abstract
In some studies, only financial aspects are emphasized, but we also see cases of assessing the financial health of municipalities through socio-economic indicators. Public organizations worldwide have had to increase their financial performance by adopting management practices. Nonetheless, financial performance might be mostly predicted by contingencies that are not within direct managerial control. The purpose of this paper is to identify clusters of municipalities on the basis of agglomerate cluster analysis, the results of which will point to the financial situation of the municipalities in the selected region. The main aim of this contribution is to identify the location of the municipalities of the chosen self-governing region of Slovakia using the clustering method by selected financial indicators. Individual clusters have similar properties and they differ from the characteristics of businesses in other clusters. The results show that organizational and environmental contingencies affect financial performance, but a significant amount of variation in financial performance is unexplained—indicating that management creates better financial health in the municipality and creates a clearer budget for the management, employees, and residents of the municipality.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".