Knack et al. [2021] Preprint Faking paper
Bibliographic record
Abstract
Self-report psychopathy scales are increasingly used in research and practice despite criticisms that they may be susceptible to response distortion and bias. We assessed the utility of including the Virtuous Responding (VR) and Deviant Responding (DR) validity scales from the Psychopathic Personality Inventory-Revised (PPI-R) for identifying underreporting and overreporting, respectively, on both the full and short-form versions of the Self-Report Psychopathy scale (SRP 4 and SRP-SF) in a pre/post experimental design. Using a sample of 384 male community members and a clinical comparison group of 99 males from a forensic outpatient program, we demonstrated that SRP scores were more susceptible to overreporting than underreporting, and that overreporting significantly and negatively affected convergent validity. Finally, baseline psychopathy scores were unrelated to successful response distortion (i.e., changing scores in correct direction while remaining undetected by the validity scales). It is recommended that assessments using self-report psychopathy scales consider including validity indices to detect response distortion. In doing so, it will be important to consider that general impression management may be conceptually distinct from specific forms of response distortion, such as the intentional amplification or minimization of psychopathic traits.
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 | 0.005 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.108 | 0.028 |
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".