What would you know about it? Managing ingroup vs. outgroup perceived support of same-sex vs. mixed-sex romantic relationships
Bibliographic record
Abstract
Social support for relationships is an important predictor of relationship well-being, duration, and mental and physical health. But does the source of the support matter? This article examines whether there is potential moderation by the ingroup versus outgroup status of the person offering the support. Specifically, in a sample of 407 individuals in mixed-sex versus same-sex relationships, we assessed how much social support individuals perceived for their relationship from members of their social network who were queer versus straight. Those in same-sex relationships had significantly more queer members within their social networks than those in mixed-sex relationships (38.6% vs. 8.1%). We found that although those in both types of relationships perceived more support for their relationships from ingroup network members, only those in mixed-sex relationships demonstrated a stronger association between ingroup (straight) support and relationship well-being. Those in same-sex relationships showed relatively weak associations between network members’ support for the relationship and their relationship well-being, regardless of source. Results are interpreted in light of the social identity perspective, and we also discuss the potential for attributional ambiguity and discounting of others’ opinions about their relationships within the context of same-sex relationships.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".