Correlates of love in intimate relationships across gender, sex of partner, marital status, and cultures
Bibliographic record
Abstract
Correlates of love were analyzed using data from an on-going cross-cultural study of intimate relationships in multiple languages at http://web.whittier.edu/chill/ir. The online questionnaire asked about one's social background, attitudes and values, current intimate relationship (if any), and well-being. Participants were recruited in North America (United States, Canada, and Mexico), South America (Argentina and Colombia), and Europe (Spain, Italy, and Romania). Others responded from 41 additional countries. Among the 3048 respondents, 71% were women, and 73% were in a relationship (74% opposite-sex and 26% same-sex, including 11% opposite-sex marriages and 9% same-sex marriages). Love was measured using a single-factor scale with 11 items from Rubin (1973), Sternberg (1986), and Lee (1973). For both sexes, love was positively correlated with perceptions of similarity, approval by parents and others, ratings of the partner's personal characteristics, frequency of communication, expressions of affection, and sexual satisfaction. These correlates were similar for both opposite-sex and same-sex unmarried samples, and for both opposite-sex and same-sex married samples, which were recruited in the US. They were also similar for unmarried opposite-sex samples that were recruited in seven other countries. In sum, correlates of love were similar across gender, sex of partner, marital status, and cultures of these countries.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".