Compersion: When Jealousy-inducing Situations Don’t (Just) Induce Jealousy
Bibliographic record
Abstract
Emotional reactions to a partner’s extradyadic romantic interests are assumed to be negative and characterized by jealousy, an emotional state that arises over a perceived threat to one’s relationship. Yet, reactions may also be positive, and involve compersion, or taking joy in one's partner's pleasure in other sexual and relational encounters. Although some have argued that compersion is the opposite of jealousy, research has shown that compersion and jealousy are not opposing constructs, despite being treated this way in both theoretical and empirical research. Using a convenience sample of polyamorous (N = 3,530) and monogamous (N = 1,358) individuals we draw on theories of jealousy, emotional ambivalence, and emotional forecasting to examine people’s anticipated affective responses to hypothetical situations involving a partner’s extradyadic relations, and assess whether experience with having a partner engage in extradyadic relations was associated with anticipating less jealousy and more compersion. Results suggest that people in polyamorous relationships report less jealousy and more compersion with their partners, and that personal experience involving a partner’s extradyadic romantic interests predicted more compersion and less jealousy, with experience predicting greater increases in compersion among monogamous than polyamorous participants. Finally, while anticipated compersion was associated with greater relationship satisfaction, neither jealousy nor ambivalence were associated with relationship satisfaction. These results further demonstrate that individuals can experience both positive and negative reactions to a partner’s extradyadic relations, both based on actual experience and projection of responses to future events, and that real-life experiences are important in anticipating these emotions.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".