1 Cross-Cultural Patterns of Interracial Marriage
Bibliographic record
Abstract
This paper compares patterns of interracial marriage in seven different cultural contexts. Four aspects of intermarriage are considered. First, log-linear models are as estimated to gage the extent of overall homogamy and race specific homogamy in each setting. Second, residuals from these models are used to assess gender differences in intermarriage. Multinomial logistic regression is then used to evaluate age and educational differences. Age is included as a surrogate for trends over time, and education is included as a measure of social status. We hypothesize that intermarriage will increase over time-but not necessarily at the same rate for each racial group or racial category. We also hypothesize that higher education is associated with higher rates of outmarriage from low-status groups, but lower rates of outmarriage from high-status groups. In other words, the average difference in status between two groups will be associated with the degree of influence education has on intermarriage. Census data were obtained for each of the cultural contexts. We begin with the United States because a substantial body of research focuses on the United States. Canada
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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 | 0.000 | 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 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".