Bibliographic record
Abstract
The aim of this thesis is to evaluate the system of municipality incomes in the Czech Republic with respect to specifics of small municipalities. In last few years these municipalities have become a frequently discussed topic because of their lower incomes. The legislation has been devoting to this problem since 2008 and the differences between large and small municipalities in the Czech Republic were removed. There is income analysis of all municipalities in the Czech Republic from 2000 up to 2012 in this thesis. The municipalities were divided into two groups. The first group contains small villages, defined as municipalities up to 499 inhabitants. In the second group there are other municipalities of the Czech Republic except small villages, statutory cities and the Capital City of Prague. The real data were recalculated per head for this analysis and minimum, maximum, median and arithmetic mean were found. Variability of incomes is evaluated from these results. Regression and correlation analysis is made in some groups of revenues because of finding dependence among variables. Disparity in incomes of small towns and other communities is assessed by comparing the arithmetic means and medians between groups of municipalities using a Two-Sample t-test and Mann-Whitney test. Revenue growth is evaluated determination of the trends of the time series using linear regression. Some specifics in financing small villages were found from these analyzes. There was also confirmation of the hypothesis that small municipalities have lower incomes per an inhabitant.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".