On the strength of ties that bind: Measuring the strength of norms in romantic relationships
Bibliographic record
Abstract
In seven studies ( n cross-sectional = 1,699, n longitudinal = 118), we developed a measure of relationship norm strength defined as qualities that make the rules and expectations in romantic couples more or less likely to be followed. In our six cross-sectional samples, the resulting Relationship Norm Strength Questionnaire (RNSQ) yielded consistent norm tractability, norm agreement, anticipated punishment for deviance, and norm explicitness factors, and estimated factors generally demonstrated evidence of convergent, discriminant, and criterion validity. Meta-analyzed effects across these samples—yielding more reliable and generalizable estimates—indicated that greater norm tractability and norm agreement were strongly linked to higher levels of relationship quality. Further supporting our model of relationship norm functioning, results from our 8-week longitudinal study of community members in relationships indicated that greater levels of norm tractability and agreement resulted in greater subsequent norm conformity. Taken together, our results suggest that relationship norm strength offers a promising new perspective on relational well-being and can add to a more comprehensive account of normative processes in close 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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".