Bibliographic record
Abstract
Abstract Objective: This study describes how the intermarriage behavior of Hispanic racial groups changed between 1990 and 2017. Background: Prior work on intermarriage trends has seldom examined the role of race in the mate selection behavior of Hispanics. The following three questions of fundamental importance have thus remained unanswered: (a) Did the intermarriage of all Hispanic racial groups slow down during the 1990s? (b) Did this trend persist after the 1990s? (c) Did the solidification and possible racialization of Hispanic pan‐ethnic identity contribute to changes in Hispanic intermarriage behavior over time? Methods: Using data from the 1990 U.S. Census, 2000 U.S. Census, and 2008 to 2017 American Community Survey, the author conducted log‐linear analyses to examine changes in the relative permeability of racial and pan‐ethnic boundaries between 1990 and 2017. Results: For Hispanic Whites, the odds of intermarriage with non‐Hispanic Whites remained unchanged during the 1990 but bounced back after 2000. For Hispanic Blacks, the corresponding odds increased steadily after 1990. For Hispanics who rejected official U.S. racial categories, the odds of intermarriage with non‐Hispanic Whites decreased over time. Conclusion: For Hispanic Whites, rates of intermarriages with non‐Hispanic Whites decreased during the 1990s in large part due to the surge of immigration from Latin America. Hispanic ethnicity may have become racialized over time for Hispanics who rejected official U.S. racial categories, but not Hispanic Whites and Hispanic Blacks. Implications: The findings from this study underscore the importance of considering racial heterogeneity when studying Hispanic intermarriage behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".