Jealousy in interracial and same-race relationships
Bibliographic record
Abstract
Interracial relationships have been on the rise and face unique relational challenges but are underrepresented in relationship science which has relied heavily on studies of same-race White couples. Existing research has shown that individuals in interracial relationships experience greater jealousy than those in same-race relationships, but these studies were underpowered or relied on binary measures of jealousy. In a large sample of individuals in interracial ( N = 196) and same-race relationships ( N = 198) from the United States and Canada, we found that individuals in interracial relationships reported experiencing jealousy more frequently and intensely (general jealousy), had greater worries about potential romantic rivals (rival-directed cognitive jealousy), and felt more distrust and anger toward rivals (rival-directed emotional jealousy). However, there were no differences in the extent to which they derogated the rival and displayed their relationship in front of the rival (rival-directed behavioral jealousy), and the findings for general and cognitive jealousy became nonsignificant when controlling for attachment anxiety. Finally, having a stronger couple identity attenuated the negative effects of having higher general jealousy and cognitive jealousy on relationship satisfaction for individuals in interracial (but not same-race) relationships. Future research should explore the development of attachment anxiety in interracial relationships and explore strategies in addition to having a stronger couple identity that can help interracial couples navigate third-party threats more effectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".