Psychopathy: Exploring Canadian Mass Newspaper Representations Thereof and Violent Offender Talk Thereon
Bibliographic record
Abstract
This social constructionist program of inquiry begins to explore how psychopathy/the psychopath is constructed beyond the professional domain of forensic psychology. Indeed, while this highly important diagnostic construct is defined and operationalized very precisely by contemporary forensic psychologists, it is believed to be grossly and seriously misunderstood by others. Study 1 examines how Canadian mass newspaper (news) discourse represents psychopathy/the psychopath using ethnographic media analysis. This study rests on the central assumption that mass newspaper discourse provides a key window onto the public construction of reality. Study 2 examines how in-treatment, persistently violent male offenders (individuals with close ‘proximity’ to psychopathy) may conceptualize, experience, and approach (or not) the diagnostic construct, as gleaned through their conversational talk during small-size focus group interviews. The various ways in which these distinct (and contextually-bound) discourses align with and diverge from one another are identified. The various ways in which mass newspaper and offender focus group discourses align with and diverge from the contemporary forensic psychological construction of psychopathy/the psychopath are also discussed. Clinical, practical, and ethical implications of the research findings are also presented and discussed briefly.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".