Benevolent cognitions as a strategy of relationship maintenance: "Don't sweat the small stuff"....But it is not all small stuff.
Bibliographic record
Abstract
To maintain intimate relationships in the face of negative experiences, many recommend cognitive strategies that minimize the implications of those experiences for global evaluations of the relationship. But are such strategies always adaptive? Suggesting otherwise, 2 longitudinal studies spanning the 1st 4 years of 251 new marriages revealed that the effects of benevolent cognitions on relationship development depended on the initial levels of negativity in the relationship. Cross-sectionally, the tendency to make positive attributions or otherwise disengage global evaluations of the relationship from negative experiences was associated with higher levels of satisfaction in marriages characterized by more frequent negative behavior and more severe problems. Longitudinally, in contrast, such strategies only demonstrated benefits to healthier marriages, whereas they predicted steeper declines in satisfaction among spouses in more troubled marriages by allowing marital problems to worsen over time. These findings highlight the limits of purely cognitive theories of relationship maintenance and suggest that widely recommended strategies for improving relationships may harm vulnerable couples by weakening their motivations to address their problems directly.
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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 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".