Examining the Relationship Between Personality and Perceived Vulnerability: Unpacking Self and Observer Perceptions
Bibliographic record
Abstract
Research examining victimization risk has demonstrated that personality variables like psychopathy may be advantageous in accurately predicting vulnerability from behaviour (Book et al., 2013). There is evidence suggesting certain behaviours and personality traits may be associated with vulnerability to victimization (Ellrich & Baier, 2016; Hall et al., 2006). The current studies aimed to assess differences in behaviours (verbal/nonverbal) in relation to perceptions of vulnerability (Study 1). Additionally, it examined the role of psychopathy and gender in accurately predicting perceived future vulnerability, and the use of behavioural cues in making vulnerability predictions (Study 2). Results from Study 1 suggest people with neurotic traits view themselves as more vulnerability to future sexual victimization, and women (vs. men) feel more vulnerable to victimization. Study 2 indicates those scoring higher on psychopathy make less accurate vulnerability predictions, and use more behavioural cues to predict vulnerability. Implications and future research avenues examining vulnerability are discussed.
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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 | 0.003 | 0.013 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".