Age-heterogamous partnerships: Prevalence and partner differences by marital status and gender composition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: We examine age heterogamy in the United States and its associations with other partnership characteristics following the nationwide legalization of same-sex marriage in 2015. METHODS: We 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, man-woman, 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. RESULTS: The 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 man-man 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 man-woman 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 man-man and man-woman couples but not for woman-woman couples. CONTRIBUTION: Man-man couples have higher rates of age heterogamy, and partner differences related to education, income, and race/ethnicity are tied to age heterogamy for man-man couples more strongly than for other couple types. Partnering patterns for man-man couples are distinct from other couple types.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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