Die Vermögenswirtschaft der Stadt im Zeitalter des Dualismus
Bibliographic record
Abstract
In the annuel budgets of the city of Pécs between 1872 and 1914, revenues from city property were divided into five groups. The first group included revenues from the city’s property – the hundreds of acres of Megyer-puszta, urban pastures, urban factories, and urban buildings. The second group included revenues from the city’s 4,262 cadastral hungarian acres forests. The third group included interest on the city’s cash and securities. The fourth group included excise, duties and fees levied by the city with the permission of the state. The most important of these were incomes from the sale of spirits, wine, beer, the holding of markets and fairs, and the use of roads and railways. The fifth group included the income that arose after the pub law was acquired by the state in 1890: state compensation and various city tax supplements. Overall, revenues from urban property in the years 1870-1880 approached, and sometimes even exceeded, 60% of budget revenues. In the 1890s, their proportion fell below 40%, increased to nearly 50% by the turn of the century, and then gradually decreased to about 30% by 1914. The result of urban wealth management has been future urbanization and infrastructure investiments, with the inevitable indebtedness at a disadvantage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".