Quarrelsome behavior in borderline personality disorder: Influence of behavioral and affective reactivity to perceptions of others.
Bibliographic record
Abstract
We examined how the amplification of 3 within-person processes (behavioral reactivity to interpersonal perceptions, affect reactivity to interpersonal perceptions, and behavioral reactivity to a person's own affect) accounts for greater quarrelsome behavior among individuals with borderline personality disorder (BPD). Using an event-contingent recording (ECR) methodology, individuals with BPD (N = 38) and community controls (N = 31) reported on their negative affect, quarrelsome behavior, and perceptions of the interaction partner's agreeable-quarrelsome behavior in interpersonal events during a 20-day period. Behavioral reactivity to negative affect was similar in both groups. However, behavioral reactivity and affect reactivity to interpersonal perceptions were elevated in individuals with BPD relative to community controls; specifically, individuals with BPD reported more quarrelsome behavior and more negative affect during interactions in which they perceived others as more cold-quarrelsome. Greater negative affect reactivity to perceptions of other's cold-quarrelsome behavior partly accounted for the increased quarrelsome behavior reported by individuals with BPD during these interactions. This pattern of results suggests a cycle in which the perception of cold-quarrelsome behavior in others triggers elevated negative affect and quarrelsome behavior in individuals with BPD, which subsequently led to more quarrelsome behavior from their interaction partners, which leads to perceptions of others as cold-quarrelsomeness, which begins the cycle anew.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".