One foot out the door: Stay/leave ambivalence predicts day‐to‐day fluctuations in commitment and intentions to end the relationship
Bibliographic record
Abstract
Abstract Why do some people maintain stable feelings of commitment toward their partners, whereas others’ feelings wax and wane from day to day? The current article draws insight from decision conflict research suggesting that individuals torn between decision options are particularly susceptible to attitude change. In three samples, we validated a stay/leave ambivalence scale to capture internal conflict about whether to remain in versus exit a relationship. In two dyadic daily experience studies, individuals who felt more ambivalent about their relationships experienced greater daily fluctuation in commitment and breakup contemplation compared to less ambivalent individuals. Ambivalent individuals’ relationship intentions were also more strongly tied to their daily experiences, such that they felt more motivated to stay on days with greater relationship positivity, and more motivated to leave on days with greater relationship negativity. We discuss implications of these results for ambivalent individuals, their partners, and our understanding of stay/leave decision processes.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".