Walking hand in hand: The role of affection-sharing in understanding the social network effect in same-sex, mixed-sex, and gender-diverse relationships
Bibliographic record
Abstract
Individuals who perceive greater support or approval for their relationships from friends and family also report greater relationship stability and commitment and better mental and physical health (known as the “social network effect”). These associations have been explained, in part, through three cognitive-affective processes: uncertainty reduction, cognitive balance, and dyadic identity formation. However, we know less about cognitive- behavioral mechanisms that might help explain the social network effect. In this study, we propose and test a model in which physical affection-sharing acts as one such behavioral mechanism. In a sample of 1848 individuals in same-sex ( n = 696), mixed-sex ( n = 1045), and gender-diverse ( n = 107) relationships, we found support for our overall model. Our findings suggest that perceived support for one’s relationships is a significant predictor of perceived support for physical affection-sharing, which in turn predicts the frequency of affection-sharing in private and public contexts and, ultimately, relationship well-being. However, we also found that relationship type moderates these associations, highlighting how the experience of sharing affection with one’s partner changes for many in marginalized relationships, especially in public. We conclude by discussing how our findings contribute to theories of social support for relationships, underscoring the importance of considering affective, cognitive, and behavioral factors relevant to the process. We also emphasize the understudied role of context in shaping affection-sharing experiences across all relationship types.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".