Wanting ‘the whole loaf’: zero-sum thinking about love is associated with prejudice against consensual non-monogamists
Bibliographic record
Abstract
Consensual non-monogamy (CNM) is a relationship in which individuals agree that romantic or sexual relationships with others are permissible or desirable (e.g. polyamory or open relationships). Although anti-CNM prejudice is prevalent, it is not well understood. We propose that one of the bases of anti-CNM prejudice is zero-sum thinking about love – the perception that one person’s love gained is another’s love lost. We outline our theory and then present three studies that test our predictions. In these studies, participants read a vignette that depicted characters who were in a CNM or monogamous relationship, and then judged aspects of the characters and their relationship. In Study 1, participants who read the CNM vignette judged the protagonist’s love for their initial romantic partner before and after they became involved with a second partner. Zero-sum thinking was operationally defined as the within-subject change in love ratings. In Studies 2 and 3, participants rated their agreement with items from a new preliminary measure of zero-sum romantic beliefs. We measured CNM devaluation by asking for ratings of the relationships and of individuals in the relationships. Supporting our predictions, in all three studies we found that zero-sum thinking about love was associated with increased CNM devaluation. We end by briefly discussing the implications of our findings.
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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 | 0.013 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".