Reconsidering Criminal Responsibility in Cases of Dissociative Identity Disorder: Legal Standards, Psychiatric Evidence, and Reform Pathways in Vietnam
Bibliographic record
Abstract
Dissociative Identity Disorder, characterized by the presence of two or more distinct identity states and recurrent memory gaps, presents significant challenges to the criminal justice system, particularly in determining criminal responsibility. Within Vietnam’s legal framework, the adjudication of DID-related cases remains underdeveloped, offering courts limited guidance. A comparative analysis of legal approaches in jurisdictions such as the United States, the United Kingdom, Australia, and Canada reveals divergent perspectives on criminal responsibility in DID cases, particularly regarding the authenticity of diagnoses and ethical considerations in adjudication. Vietnam faces substantial deficiencies in psychiatric evaluation procedures and judicial comprehension of Dissociative Identity Disorder. Addressing these challenges necessitates comprehensive legal and procedural reforms, including the formal recognition of Dissociative Identity Disorder in psychiatric assessments, the establishment of specialized diagnostic protocols, the adoption of tailored standards for criminal responsibility, the creation of dedicated forensic psychiatric institutions, and the systematic training of legal professionals. These reforms are essential to harmonizing Vietnam’s criminal justice practices with contemporary psychiatric knowledge and fundamental principles of fairness and justice.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".