The Provision and Implication of Insanity Defense in Pakistani Laws
Bibliographic record
Abstract
The current study sought to explore the provision and implications of the insanity defense in Pakistani laws. This research aims to find the lacunae in the law that aids mentally ill offenders. It also proposes to identify the role of lawyers and forensic psychologists and government institutions in the insanity defense. The study includes understanding and implementing the insanity defense in Pakistani laws and the law system. The study sample involved nine lawyers and four forensic psychologists whose interviews were conducted using a semi-structured questionnaire. Participants were contacted through the snowball sampling technique. The current study concluded that more than one key factor hindered the law system's understanding of insanity defense and mental health. The absence of psychiatric or psychological assessments in the court system suggests a common knowledge of insanity defense and mental health. The case studies overall present the different sides of mental health in Pakistan. The results also inferred that the key factors that had played the role of hindering the development of insanity defense could also enhance the development of insanity defense. The psychosocial factors that influence the hindering of insanity defense are highlighted. The results of the present study indicate a completely different point of view regarding insanity defense for lawyers and forensic psychologists, who can use this knowledge in their professional endeavors.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".