Investigating If... Then... Personality Signatures Among Narcissists
Bibliographic record
Abstract
Traditionally, personality research has separated a person’s disposition from the situation they are in. However, complex schemas, such as if… then… personality signatures, conceptualize people and their situation as interactive and mutually dependent (e.g. if situation A, then the person does X, but if situation B, then the person does Y). The present research seeks to investigate if… then… personality signatures among narcissists (those who are egotistical, self-focused, and vain). After reading about a narcissistic and a non-narcissistic target, participants will rate how warm and dominant they perceive each target will behave across a series of scenarios (e.g., in competitive settings, with friends, as a leader). We found that perceivers rated the narcissistic target as less warm and more dominant, on average, than the non-narcissistic target. There was also a significant amount of variability in participants’ ratings of target’s warmth and dominance across contexts, suggesting that people use if… then… personality signatures. Notably, these signatures differed across target type: narcissistic targets were perceived as more variable than non-narcissistic targets. In summary, people perceive narcissists as using if… then… personality signatures. That is, perceivers think of narcissists as displaying significant variability in their behaviour across contexts. Faculty Mentor: Miranda Giacomin Department: Psychology
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".