n search of the ideal husband. Could inequality in the pre-industrial era be measured through dowries? North-eastern Catalonia, 1750-1825
Bibliographic record
Abstract
This paper explores the possibilities that dowries may offer to study inequality in the pre-industrial era. We argue that, mostly, in rural societies with impartible inheritance, families competed to join the heir of an estate that would allow them to maintain or even improve their socio-economic status by means of paying the best possible dowry. Hence, dowries may be an indicator of family wealth and, therefore, disparities in dowry amounts could be informing about economic inequality. Then, we show the results of a case study based on a rural region in north-eastern Catalonia from 1750 to 1825, which suggest that over the last decades of the 18th century and the first quarter of the 19th century, inequality increased significantly. As it was a period of bellicosity and inflation, our results suggest that political instability tended to increase inequality in pre-industrial societies, as it has been previously stated by some authors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".