The Role of Defense Mechanisms in Borderline and Antisocial Personalities
Bibliographic record
Abstract
We examined whether borderline personality disorder (BPD) and antisocial personality disorder (APD) could be differentiated based on defense mechanisms as measured by observer (Defense-Q; MacGregor, Olson, Presniak, & Davidson, 2008) and self-report (Defense Style Questionnaire; Andrews, Singh, & Bond, 1993) measures. We conducted 2 studies whereby nonclinical participants were divided into borderline and antisocial groups based on scores from the Personality Assessment Inventory (Morey, 1991). Multivariate analysis of variance results revealed significant overall group differences in defense use. Univariate analyses further showed group differences on several individual defenses (e.g., acting out, denial, and turning against self). Together, the findings suggest that in BPD, the defenses may emphasize interpersonal dependency and a tendency to direct aggression toward the self; whereas in APD, the defenses may emphasize egocentricity, interpersonal exploitation, and a tendency to direct aggression toward others. Overall, this study demonstrates important differences in defense use between borderline and antisocial personality groups across both observer and self-report measures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".