Gender, self-disclosure and emotional closeness in friendships: an egocentric social network study
Bibliographic record
Abstract
Self-disclosure plays an important role in promoting emotional closeness in friendships. Some literature points to gender differences in emotional closeness and self-disclosure but findings are mixed particularly when comparing same-gender to cross-gender friendships. Our study aims to clarify previous findings by measuring broader friendship networks and taking into account each listed friend's gender. Our sample of 215 participants (egos) listed on average 8 friends (alters) in their networks (in total 1774 friends listed). For each friend, participants indicated the alter’s gender, the amount of self-disclosure they made to the alter, as measured with the McGill friendship function intimacy questionnaire, and the emotional closeness they felt with the alter. We found no gender differences in network size or gender homophily, although men and women did have gender homophilous networks (men had a larger percentage of male friends and women had a larger percentage of female friends). Multilevel mediation models demonstrated that female alters elicited greater self-disclosure with both male and female egos. Self-disclosure mediated the relationship between the gender of the alter and emotional closeness for both male and female egos such that higher self-disclosure was associated with higher emotional closeness. We discuss our findings with reference to gendered communication norms and argue for the value of considering the gender of both alters and egos in friendships.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".