Unknown Evils: Revisiting the Psychopathy/Responsibility Debate with Epistemic Injustice
Bibliographic record
Abstract
The callous and seemingly emotionless temperament possessed by individuals with psychopathic traits has caused them to be a source of fascination for psychologists, legal theorists, philosophers and laymen alike.Many authors have offered reconciliation for their harmful actions, contending that without the capacity to appreciate the wrongfulness of their action, individuals with psychopathic traits ought not be held responsible for them.In this thesis, I discuss many of these arguments, examining assertions for a mitigated attribution of both legal and moral responsibility.Additionally, I consider an unexplored aspect within the debate of psychopathy and responsibility: Miranda Fricker's concept of Epistemic Injustice.Using this concept, I contend that the current standard of holding individuals with psychopathic traits criminally responsible reflects pervasive patterns of injustice, and that individuals with psychopathic traits are not deserving of being held fully responsible for their criminal wrongdoings.Acknowledgments I would first like to express my profound gratitude to my thesis supervisor, Dr. Joshua Shepherd of the Philosophy Department at Carleton University, for keeping me positive, focused, and confident in my research.Dr. Shepherd always offered me judicious advice and guidance, whether I needed help in articulating my ideas, or even if I was struggling to find balance between my research and other responsibilities.He consistently allowed this paper to be all my own work and ideas,
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.010 |
| 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".