Who Married, (to) Whom, and Where? Trends in Marriage in the United States, 1850-1940
Bibliographic record
Abstract
We present new findings about the relationship between marriage and socioeconomic background in the United States in the late 19th and early 20th Centuries. Imputing socioeconomic status of family of origin from first names, we document a socioeconomic gradient for women in the probability of marriage and the socioeconomic status of husbands. This socioeconomic gradient becomes steeper over time. We investigate the degree to which it can be explained by occupational income divergence across geographic regions. Regional divergence explains about one half of the socioeconomic divergence in the probability of marriage, and almost all of the increase in marital sorting. Differences in urbanization rates and the share of foreign-born across states drive most of these differences, while other factors (the scholarization rate, the sex ratio and the share in manufacturing) play a smaller role.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".