Signaling virtuous victimhood as indicators of Dark Triad personalities.
Bibliographic record
Abstract
We investigate the consequences and predictors of emitting signals of victimhood and virtue. In our first three studies, we show that the virtuous victim signal can facilitate nonreciprocal resource transfer from others to the signaler. Next, we develop and validate a victim signaling scale that we combine with an established measure of virtue signaling to operationalize the virtuous victim construct. We show that individuals with Dark Triad traits-Machiavellianism, Narcissism, Psychopathy-more frequently signal virtuous victimhood, controlling for demographic and socioeconomic variables that are commonly associated with victimization in Western societies. In Study 5, we show that a specific dimension of Machiavellianism-amoral manipulation-and a form of narcissism that reflects a person's belief in their superior prosociality predict more frequent virtuous victim signaling. Studies 3, 4, and 6 test our hypothesis that the frequency of emitting virtuous victim signal predicts a person's willingness to engage in and endorse ethically questionable behaviors, such as lying to earn a bonus, intention to purchase counterfeit products and moral judgments of counterfeiters, and making exaggerated claims about being harmed in an organizational context. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".