A Motivational Framework for Psychopathy: Toward a Reconceptualization of the Disorder
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. The link between psychopathy and violence has been well documented. Estimates suggest psychopathic offenders are responsible for as much as 40% of violence-related crime, and that they show rates of violent recidivism up to five times higher than non-psychopathic offenders. Existing theories of the disorder argue that this violence stems from a core insensitivity to emotional/aversive information, or from a core inability to optimally allocate processing resources in complex environments. However, some newer findings have been difficult for existing theories to assimilate; moreover, successful treatment programs based off current conceptualizations have been slow to develop. With this in mind, the current paper proposes a new motivational framework for psychopathy, within which the disorder is conceptualized as stemming from more strategic, motivated processes. The paper begins by reviewing traditional theories of psychopathy and highlighting their explanatory strengths and limitations. The proposed motivational framework is then outlined, and a supportive rationale for the framework provided. Next, the paper undertakes a selective review of some of the most empirically supported features of the disorder, to highlight how these features may be productively reformulated within a motivational framework. Finally, the paper suggests several methods through which an empirical evaluation of the proposed ideas may be undertaken, and explores potential implications of a motivational framework for next-generation rehabilitation and treatment opportunities.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it