Land-use and rural inequality profiles in the province of Barcelona in mid-nineteenth century
Bibliographic record
Abstract
The long-term impact on income inequality of agricultural commercial specialization is still an open-ended discussion. Diverse economic models and approaches offer competing views, while historians increasingly stress the contingent nature of the paths followed in the various contexts. Applying common inequality indices like the Theil index along with new ones such as the inequality possible frontier (IPF) and Inequality Extraction Ratios (IER), this study examines how winegrowing specialization in Catalonia correlated with agr icultural income distribution in the municipalities of the province of Barcelona during the mid-nineteenth century. This analysis examines a large dataset assembled from over 86,000 cadastral taxpayers in 292 municipalities and recorded in the Distribution of Personal Wealth in Real Estate Ownership of the province of Barcelona in 1852, combined with other population and land use data listed in the Estadística ter ritor ial de la provincia de Barcelona (Land Use Statistics of the Province of Barcelona), compiled in 1858. The results confirm that inequality in agricultural income distribution was lower in predominantly winegrowing municipalities than in timber and cereal-growing ones, despite the fact that commercial specialization and higher population densities could have extended the inequality possible frontier of those wineg rowing areas in the mid-nineteenth century.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".