Verification of the application of psycho-diagnostic tool Hare Psychopathy Checklist. Contribution to typology of inmates
Bibliographic record
Abstract
In the theoretical section of my graduation thesis I deal with the development of the penal system and the classification of inmates; I look at the personality of delinquent in respect of criminogenic factors (biological, social and psychological); further I outline a number of typologies of inmates focusing on the risk assessment and the most frequently used classificatory diagnostic tools in the USA, Canada, and the Great Britain. As the group of inmates with dissocial personality disorder is considered to be high-risk, its correct diagnostics is crucial. For this reason the aim of the empirical part of my graduation thesis is the verification of the applicability of the psycho-diagnostic tool Hare Psychopathy Checklist - Revised (PCL-R): 2nd edition. My research was conducted in Vinařice prison on the sample of 36 inmates that were presumed to suffer from dissocial personality disorder. In order to verify the applicability of PCL-R in our conditions, I have chosen for the comparison already verified method of Eyseneck's questionnaire PEN. The research results confirmed the alternative hypotheses. There was proved the statistically significant dependency between the values of the total score PCL-R and the subscales of psychoticism and criminality of the questionnaire PEN, but only on the low significance...
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".