Bibliographic record
Abstract
Maintaining close relationships is an important human motivation. However, even the most stable relationships are faced with challenges and threat. Although commitment is associated with relationship stability, it is the thoughts, feelings, and behaviors that commitment promotes that help to sustain relationships in the long term. In this chapter, we review research on such relationship maintenance processes. We discuss how relationship maintenance helps committed individuals ward off relational threats like the allure of attractive alternatives and the problem of partner transgressions. We also examine the role of relationship maintenance in making personal sacrifices for one's partner and idealizing one's partner. Although the vast majority of the literature focuses on the importance and benefits of maintaining relationships, we also address circumstances in which relationship maintenance can be detrimental to the individual and to the relationship. We then outline a framework of motivational and cognitive resources that play crucial roles in sustaining and maintaining relationships. We also turn the argument around, and instead of focusing on how individuals respond to relational threats, we describe how the partner specifically influences an individual's relationship maintenance. We conclude with a brief discussion of the future of relationship maintenance research.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".