Loneliness, Sex, Romantic Jealousy, and Powerlessness
Bibliographic record
Abstract
The revised UCLA Loneliness Scale, items from a revision of an interpersonal jealousy personality scale varying in partner-rival contact, and vignettes depicting partner-rival contact were administered to 194 female and 97 male college students in Study 1. Those measures and a measure of powerlessness, comprising the learned helplessness attribution style, were administered to 44 female and 28 male college students in Study 2. Results showed that the relation between jealousy and both loneliness and powerlessness varied as a function of the type of contact between romantic partner and rival. Loneliness and powerlessness were: (i) positively associated with jealousy for unilateral contact (e.g., a partner admiring an opposite-sex person), (ii) not associated with jealousy for bilateral contact (e.g., a partner having an opposite-sex person as a friend), and (iii) negatively associated with jealousy for mutual contact (e.g., a partner kissing an opposite-sex person). Regression analyses indicated that powerlessness mediated, in part, the relation between loneliness and jealousy. Although some sex differences were found in jealousy, those differences did not conform to the pattern expected on the basis of sex differences in powerlessness. It was proposed that lonely individuals tended to display situationally inappropriate jealousy because of their powerlessness and that tendency posed a problem for their romantic relationships.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".