A qualitative analysis of on-again/off-again romantic relationships: “It’s up and down, all around”
Bibliographic record
Abstract
Although relational research predominantly conceptualizes romantic relationships as either together or apart, some relationships break up and renew (i.e., on-again/off-again relationships). Partners’ accounts of on-again/off-again relational experiences were qualitatively analyzed to explore both reasons for breakups and reasons for renewals. Themes were interpreted within an interdependence framework to explain why partners dissolved as well as renewed their relationships. The themes in combination suggest renewals occurred due to dissatisfying experiences with alternative relationship partners and an increase in outcomes (i.e., rewards minus costs) after breakups. Partners’ post-dissolution contact and their uncertainty about relational status may have further facilitated renewals. More generally, the themes suggest, for on-again/ off-again partners, breakups did not indicate the end of interdependence but rather a redefinition of the relationship.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".