The reputational consequences of victim signaling
Bibliographic record
Abstract
We examine how victim signaling, defined as publicly sharing experiences of suffering caused by disadvantage, harm, or limitations, affects how observers perceive the signaler. We conducted four studies ( N Total = 1430) on diverse samples (i.e., online participants and professionals in the Philippines), using different methodologies (i.e., employee-coworker dyads and vignette-based experiments), and ways of victim-signaling (i.e., contentious vs. subtle). Across contexts, we found that people who signal their victimhood were evaluated more negatively than those who did not emit this signal, despite the latter facing similar circumstances. We found this effect on a range of social judgments, including ratings of dark traits (Dark Triad and D) and perceived desirability of the signaler as a social partner (e.g., job performance ratings and perceptions of counterproductive workplace behavior). A post-hoc analysis in Studies 3 and 4 found that political beliefs moderated perceptions of victim signalers from minority groups; compared to conservatives, liberals were less likely to see victimhood signalers (vs. non-signalers) as narcissistic and psychopathic (Study 3) and were less likely to infer entitlement–Machiavellian traits from a victim-signaling candidate (Study 4). Our results contribute to understanding how victim signaling shapes social perception and the complexities of interpreting claims of harm. • Individuals who signal their victimhood are often perceived less favorably than those who do not. • Victim signaling is associated with inferences of dark personality traits and lower social desirability. • Public expressions of harm can lead to reputational costs. • Observers may infer unethical behavior from victim signaling.
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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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".