Age-heterogamous partnerships: Prevalence and partner differences by marital status and gender composition
Bibliographic record
Abstract
OBJECTIVEWe examine age heterogamy in the United States and its associations with other partnership characteristics following the nationwide legalization of same-sex marriage in 2015. METHODSWe use American Community Survey data for 2017-2021 to examine age gaps in over 3.3 million couples, differentiating by couple gender composition (man-man, manwoman, woman-woman) and marital status (cohabiting, married).We estimate the prevalence of age heterogamy and how it correlates with education, income, and race/ethnicity differences between partners. RESULTSThe prevalence of age heterogamy and its associations with other partner differences vary by couple gender composition and marital status.Man-man couples have higher rates of age heterogamy than man-woman and woman-woman couples; over three in ten manman couples had age gaps of at least eight years between partners, with no difference by marital status.Age heterogamy was less common among married than cohabiting manwoman couples.For most couple types, educational and income differences between partners were more common among age-heterogamous partnerships.The prevalence of interracial/interethnic partnerships was higher among age-heterogamous married manman and man-woman couples but not for woman-woman couples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".