Only because I love you: Why people make and why they break promises in romantic relationships.
Bibliographic record
Abstract
People make and break promises frequently in interpersonal relationships. In this article, we investigate the processes leading up to making promises and the processes involved in keeping them. Across 4 studies, we demonstrate that people who had the most positive relationship feelings and who were most motivated to be responsive to the partner's needs made bigger promises than did other people but were not any better at keeping them. Instead, promisers' self-regulation skills, such as trait conscientiousness, predicted the extent to which promises were kept or broken. In a causal test of our hypotheses, participants who were focused on their feelings for their partner promised more, whereas participants who generated a plan of self-regulation followed through more on their promises. Thus, people were making promises for very different reasons (positive relationship feelings, responsiveness motivation) than what made them keep these promises (self-regulation skills). Ironically, then, those who are most motivated to be responsive may be most likely to break their romantic promises, as they are making ambitious commitments they will later be unable to keep.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".