Regional Disparities in Hungary in the Era of Absolutism and the Dualistic Period (1780–1914): A Statistical Analysis to Assess Inequalities and the Driving Forces of Development
Bibliographic record
Abstract
Abstract This article aims to reevaluate different socioeconomic and political systems—the era of Habsburg absolutism and the reconstruction after the expulsion of the Ottomans (1720–80s), the age of reforms (1780–1880), and the beginnings of the capitalist development in the Kingdom of Hungary (1880–1914)—from a different perspective: by focusing on trends in inequalities and changes in development levels. In order to trace the changes in the pattern of backward areas, development was calculated for three time horizons relying on the settlement-level data of the GISta Hungarorum database. The novelty of our approach is that we consider development multidimensionally, therefore a composite indicator of development was calculated using a causal approach (Structural Equation Modeling [SEM]) to substitute for Gross Domestic Product (GDP) or Human Development Index (HDI), which cannot be reconstructed at fine scale, nor calculated for the eighteenth century. Regional patterns of core-periphery relations and their temporal changes (differences in dynamism) were analyzed using GIS-methods for the three time horizons, and the varying depth of backwardness was also investigated by tracing two U-curves of the type developed by Jeffrey Williamson and Simon Kuznets over three centuries. Special attention has been paid to the relationship between nationalities and development levels in our analysis, as state politics has been accused of a discriminative attitude toward national minorities.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".