Pathological jealousy and pathological love: Apples to apples or apples to oranges?
Bibliographic record
Abstract
Pathological jealousy evokes emotions, thoughts, and behaviors that cause damage to social and interpersonal relationships. On the other hand, pathological love is the uncontrollable behavior of caring for a partner that results in neglecting the needs of the self. The aim of the present research was to assess the similarities and differences between the two psychopathologies of love. To this end, thirty-two individuals with pathological jealousy and 33 individuals with pathological love were compared on demographics, aspects of romantic relationship (jealousy, satisfaction, love style), psychiatric co-morbidities, personality and psychological characteristics (e.g., impulsivity). In a univariate analysis individuals with pathological jealousy were more likely to be in a current relationship and reported greater satisfaction. The avoidant attachment and the ludus love style were associated with pathological jealousy whereas the secure attachment and agape love style was associated with pathological love. Almost three-quarters (72.3%) of the sample met criteria for a current psychiatric disorder, however no differences emerged between the pathological jealousy and pathological love groups. In a binary logistic regression, relationship status and impairments in parenting significantly differentiated the groups. While both pathological jealousy and pathological love share similarities, they also present with unique differences, which may have important treatment implications.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".