Long-Term Changes in the Spatial Pattern of Quality of Life in Hungary Since the Beginning of the Twentieth Century
Bibliographic record
Abstract
Abstract Long-term analyses frequently generate major methodological challenges. In the current study, an international indicator has been used for the case of Hungary. The modified and adapted so-called historical settlement-level Human Development Index (hsHDI) provides the methodological frame for the study. HDI is based on three components: GDP/income, educational attainment, and life expectancy. Five periods were included in the analysis. Significant changes were detected during this more than one-century-long period in the locations of the most and least developed settlements. The spatial pattern of hsHDI indicates a better situation in Transdanubia, while extended backward areas fall along the current border area even before the 1920 Trianon Peace Treaty. The Communist era had a major impact on the spatial structure, and increased living-quality values were observed in the northern Hungarian territory due to the state-supported mining and heavy industries. During the last decades—most occurring after the change of regime in 1989—significant restructuring was detected in the backward and developed settlements. The spatial structure of quality of life demonstrated increasing concentrations of high values in the surroundings of Budapest and the largest towns; low values in southwestern and northeastern Hungary were also observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".