Evaluation of the Financial Performance of the Municipalities in Slovakia in the Context of Multidimensional Statistics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 it