<scp>Breaking‐up</scp> is hard to study: A review of two decades of dissolution research
Bibliographic record
Abstract
Abstract The dissolution of romantic relationships can be conceptualized in many ways, from a distressing event or a consequential life decision to a metric of a relationship's success. In the current review, we assess how relationship science has approached dissolution research over roughly the past 20 years. We identified 207 studies (from 195 papers) published between 2002–2020 that captured relationship dissolution events and coded the papers for relevant features. The most common methodological approach to studying breakups was a self‐report study (92%) in which relationships were tracked over time (72%) and breakups were treated as an outcome variable (79%). These results suggest that most research on dissolution has focused on predictors of it, rather than processes required to uncouple and circumstances surrounding the breakup itself. Coding revealed heterogeneous theoretical approaches, with the most common perspective across papers—social exchange/interdependence theory—informing only 15% of the papers coded. A majority (61%) of samples were representative of the nations, regions, or localities in which the studies were conducted. Yet, samples still tended to be disproportionately comprised of young, white individuals from Western countries. We conclude by discussing potential avenues for moving our understanding of relationship dissolution forward.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".