Manipulation and Instability: Exploring Machiavellianism and Borderline Personality Similarities and Differences
Bibliographic record
Abstract
Machiavellianism and borderline personality are known for influencing interpersonal dynamics through manipulative behaviors. Machiavellianism is characterized by calculated, egotistic, and callous manipulation, while borderline personality involves emotionally driven impulsive manipulation due to instability and fear of abandonment. In this study, we explored the relationships of the two constructs with respect to broader personality constructs. Adult participants (N = 1011; Mage = 49.08 years, SD = 17.15) completed two measures each for Machiavellianism and borderline personality and a single inventory measuring the Big Five personality traits. Latent Profile Analysis (LPA) was used to investigate subgroups within the data. Machiavellianism was more strongly negatively associated with agreeableness and conscientiousness, while borderline personality traits were more strongly linked to neuroticism (more positively), agreeableness, and conscientiousness (both more negatively). Two distinct latent profiles emerged. Based on these findings, we suggest that Machiavellianism can align with either adaptive or maladaptive functioning, whereas a combination of Machiavellianism and borderline personality traits underscores a tendency towards manipulative behaviors with emotional instability. We suggest that future research build upon our findings by investigating concrete manipulative acts predicted by borderline personality and Machiavellianism.
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 | 0.000 |
| 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".