Are Cognitive Distortions Associated With Denial and Minimization Among Sex Offenders?
Bibliographic record
Abstract
Although there has been much speculation about the relationship between cognitive distortions and denial/minimization, little research on the subject is available. The authors conducted secondary analyses on existing data sets to further examine the degree of association between various measures of cognitive distortions and denial/minimization among child molesters (Sample 1, n = 73; Sample 2, n = 42; Sample 3, n = 38) and rapists (Sample 1, n = 41; Sample 3, n = 14). Meta-analysis of the findings from Samples 1, 2, and 3 indicated that greater endorsement of cognitive distortions about sex offending in general was significantly associated with greater denial/minimization of one's own guilt and deviance (r = .24), harm to one's own victims (r = .32), one's need for treatment (r = .21), and responsibility for one's sex offenses (r = .16). Although correlated, cognitive distortions and denial/minimization, at least as typically measured, are distinct constructs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".