Bibliographic record
Abstract
In 17 countries suicide is currently illegal, and an additional 20 countries follow Islamic or Sharia law where suicide attempters may be punished with jail sentences. The majority of countries have laws making it illegal to abet, aid or encourage suicide, but the nature and punishment of the illegal actions varies. Laws in places with civil, common law, Islamic law and traditional law systems are compared. Great variances in application were noted, sometimes within countries. It is impossible to estimate the number of persons currently in jail for attempted suicide, but jail sentences are still given. Some countries do not prosecute suicide attempters despite the laws, while others consistently jail suicide attempters. In countries where suicide attempts have been decriminalised, attempters may still face prosecution when another person is injured or dies as a result of their suicide attempt, or when the attempter is a member of the military. We discuss the roots of laws making suicide, aiding and encouraging suicide illegal and examine prospects for future changes. The recent Supreme Court Decision in Canada, invalidating the law making it illegal to assist in the suicide of physically ill people who are suffering (albeit with restrictive conditions) illustrates current trends towards “liberalisation” of assisted suicide.
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 | 0.003 |
| 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 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".