#AbolishNCR: A Qualitative Analysis of Social Media Narratives around the Insanity Defense
Bibliographic record
Abstract
This article presents an analysis of social media posts by laypersons regarding a finding of Not Criminally Responsible on Account of Mental Disorder (NCRMD) for Matthew de Grood after a high-profile trial in 2016 in Canada. From trial to verdict, a total of 4,991 tweets relating to the case were harvested from Twitter. Qualitative content analysis of 365 tweets by laypersons revealed three themes – largely equating the insanity defense to a legal loophole: (1) The case exemplified a misappropriation of the legal defense (e.g., due to privilege, due to the seriousness of the offence); (2) The perception existed that the NCRMD defence is a miscarriage of justice; (3) Many comments reflected a search for answers and justice. These embodied the ABCs of NCRMD: advocating, blaming, and clarifying. A need for public education about the forensic psychiatric system is evident; misconceptions about the insanity defence appeared pervasive. Further research could focus on the efficacy of knowledge translation over new media channels, such as Twitter.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".