So where do you see this going? The effects of commitment asymmetry and asynchrony on relationship satisfaction and break‐up
Bibliographic record
Abstract
Abstract Discrepancies in partners’ commitment have been emphasized as a key factor involved in relationship instability. We tested the contributions of multiple types of commitment asymmetry (discrepancies between partners at one time point) and asynchrony (discrepancies in the progression of commitment over time) to relationship satisfaction and break‐up. In three studies (N = 6960 couples) spanning months (Study 1), days (Study 2) and years (Study 3), commitment asymmetry and asynchrony consistently did not predict satisfaction or break‐up when controlling for individuals and their partners’ commitment. Only one's own commitment and proportion of downturns in commitment (reporting lower commitment than the previous time point) consistently predicted satisfaction. Women's (but not men's) commitment and proportion of downturns were associated (negatively and positively, respectively) with break‐up. Thus, contrary to some significant previous findings, commitment asymmetry and asynchrony are not indicative of future relationship outcomes. Our findings have important implications for theoretical models of commitment and couples’ practical issues in relationships 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 | 0.003 | 0.022 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".