How interdependent are stay/leave decisions? On staying in the relationship for the sake of the romantic partner.
Bibliographic record
Abstract
The decision to end a romantic relationship can have a life-changing impact on the partner as well as the self. Research on close relationships has thus far focused on self-interested reasons why people choose to stay in their relationship versus leave. However, a growing body of research on decision-making and prosociality shows that when people make decisions that impact others, they take those others' feelings and perspectives into consideration. In the present research, we tested the prediction that people make stay/leave decisions prosocially, such that consideration for their romantic partner's feelings can discourage people from ending their relationships. In Study 1, a total of 1,348 participants in romantic relationships were tracked over a 10-week period. Study 2 was a preregistered replication and extension of Study 1, in which 500 participants contemplating a breakup were followed over a 2-month period. Both studies showed that the more dependent people believed their partner was on the relationship, the less likely they were to initiate a breakup. These findings held above and beyond a variety of self-focused variables (e.g., investment model components; Rusbult, Martz, & Agnew, 1998). These results suggest that people can be motivated to stay in relatively unfulfilling relationships for the sake of their romantic partner. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".