Relationship satisfaction and the subjective distance of past relational events
Bibliographic record
Abstract
The present research examines how the subjective time of relational memories is linked to present relationship satisfaction. We tested the hypothesis that satisfied (but not dissatisfied) partners would keep happy relational events subjectively close in time and relegate transgressions to the subjectively distant past (regardless of when those events actually occurred). We found support for our predictions in the context of romantic relationships (Study 1) and with any type of close other (e.g., friends, family members; Study 2). To better understand the implications of the subjective distancing pattern among highly satisfied versus dissatisfied partners, we examined the role of perceptions of event importance. We found that highly satisfied partners’ adaptive pattern of distancing mediates their tendency to ascribe continued importance to past relationship glories, while dismissing earlier relational disappointments as unimportant (Study 2). We then examined the causal impact of subjective time on importance and on subsequent relationship satisfaction by manipulating both event valence and perceptions of subjective distance (Study 3). People were more satisfied when happy relational events felt close and unhappy ones felt distant. This work sheds light on a reciprocal process whereby highly satisfied partners navigate the temporal landscape of their relational histories by retaining and valuing happy memories and by discarding the relevance of painful ones, which then maintains or boosts subsequent relationship satisfaction.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".