Bibliographic record
Abstract
Abstract Objective This study examines, for the first time in Canada, the relationship between how different‐sex couples meet and assortative mating on education, race, nativity, and age. Background Extending research on how the likelihood of heterogamy differed between offline and online dating, this study disentangles the implications of institutional and third‐person influences from those of online dating for configuring the patterns of heterogamy and gender asymmetry in assortative mating. Method Data from a 2018 national survey are analyzed using (multinomial) logit models. Results Educational heterogamy and nativity heterogamy are higher, but age heterogamy appears lower, in online than offline dating. Next, specific channels of offline dating—formal institutions, social ties, and other channels—are distinguished and compared with online dating. Online dating tends to entail higher educational and nativity heterogamy (vs. meeting through formal institutions), higher racial and nativity heterogamy but lower age heterogamy (vs. meeting through social ties), and higher educational heterogamy (vs. meeting through other offline channels). Further considering gender asymmetry shows that online dating is associated with higher educational hypergyny (more‐educated man, less‐educated woman) than meeting through other offline channels; higher nativity hypogyny (immigrant man, native‐born woman) than meeting offline (overall, formal institutions, social ties); and lower age hypergyny (older man, younger woman) than meeting offline through social ties. Conclusion The findings help untangle the roles of institutional, social, and digital forces in shaping assortative mating. They illustrate the importance of leveraging theoretically informed comparisons to understand how online and offline dating configures assortative mating and its gender‐asymmetric patterns.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".