Patterns and Determinants of Wealth Inequality in Late-Nineteenth-Century Ontario
Why this work is in the frame
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Bibliographic record
Abstract
This article shows that late-nineteenth-century wealth inequality was associated with rising wealth levels supporting the existence of a Kuznets-type curve, but this curve is not unconditional.The tendency of wealth inequality to vary with age means that wealth inequality was also a function of the changing age composition of the population and may have been the result of portfolio allocation decisions across the life cycle. Canada's population “aged” during the late nineteenth century, with the proportion of population under age 20 dropping from 53% in 1871 to 43% by 1911. The general aging of the population could have increased inequality in both wealth and income. These results follow recent work by Jeffrey Williamson (1998), who argues that Kuznets curves are not unconditional. In other words, as the results of this article confirm, wealth inequality is the outcome of a complex economic process, not a single determinant cause.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it