Bibliographic record
Abstract
Involuntary commitment of the mentally ill and forced treatment of suicidal persons are practiced worldwide, with underlying premises that contrast with the respect for autonomy upon which Medical Assistance in Dying (MAiD) (euthanasia and assisted suicide) for the mentally ill is based. We trace the transition from paternalistic mass incarcerations to hospitalisation only for dangerousness. In response to criticisms that predicting dangerousness is indefensibly inexact, criteria have shifted to emphasise incompetence. In carceral institutions with inhumane conditions, controversial forced feeding protocols pit the desire to save lives against forced living with extreme suffering. As MAiD for persons suffering from a mental disorder is increasingly debated, arguments in favour focus on recognition of the capacity for self-determination, the benevolence of ending interminable suffering and MAiD as a human right which the mentally ill should be able to access without discrimination. Opponents cite research on the unpredictable course of mental disorders and inability to predict when the disorder is irremediable. They emphasise pervasive ambivalence in suicidal desires and that legalising MAiD for mental illness is inherently stigmatising.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".