Speech of young offenders as a function of their psychopathic tendencies.
Bibliographic record
Abstract
The purpose of this study was to analyse young psychopathic offenders' speech compared with controls and to determine whether it was dissimilar. An examination of two subsets of disfluencies in speech was conducted (i.e., filled pauses and discourse markers) to explore their disfluent language. Transcripts of Psychopathy Checklist-Revised Youth Version (PCL:YV) interviews from a sample of young offenders were analysed using Wmatrix software (Rayson, 2003, 2008). The young offenders were divided into a high psychopathy group (HP; n = 13) and a low psychopathy group (LP; n = 13). HP participants included more words relating to basic needs (i.e., money, sex) in their speech than their counterparts, but not fewer words relating to social needs (i.e., family, kin), which could reflect viewing the world in a more unemotional and instrumental way by HP individuals compared with LP participants. HP participants had fewer total disfluencies and filled pauses (i.e., uh, um) in their speech than LP participants. However, the usage of discourse markers (i.e., I mean, you know, like) was similar for HP and LP participants. Like adult psychopaths, the young offenders with higher psychopathic tendencies tended to use more basic needs words in their speech. Reduced filled pause use, which has been found to be related to individual's self-consciousness, may reflect less self-monitoring in psychopaths when they are engaging in secondary tasks (i.e., tasks that will not offer rewards). These findings provide further support that individual differences can be reflected by characteristics in speech. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".