How Love Emerges in Arranged Marriages: Two Cross-cultural Studies
Bibliographic record
Abstract
Two studies (N = 52) examined how love emerged in arranged marriages involving participants from 12 different countries of origin and 6 different religions. The first study (n = 30), mainly qualitative in design, found that selfreported love grew from a mean of 3.9 to 8.5 on a 10-point scale. A number of factors were identified that appeared to contribute to the growth of love, the most important of which was commitment. In the second study (n = 22), mainly quantitative in design, 36 factors that might contribute to the growth of love were assessed, with participants indicating on a 13-point scale (from -6 to +6) whether each factor made their love grow weaker or stronger. Love grew from a mean of 5.1 to 9.2, and sacrifice and commitment emerged as the most powerful factors in strengthening love. These and other factors appear to work because they make people feel vulnerable in each other’s presence, a hypothesis that is consistent with a growing body of laboratory research. The fact that love can grow in some arranged marriages—and that this process can apparently be analyzed and understood scientifically—raises the possibility that practices that are used to strengthen love in arranged marriages could be introduced into autonomous marriages in Western cultures, where love normally weakens over time.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".