Pereira’s Attack on Legalizing Euthanasia or Assisted Suicide: Smoke and Mirrors
Bibliographic record
Abstract
OBJECTIVE: To review the empirical claims made in: Pereira J. Legalizing euthanasia or assisted suicide: the illusion of safeguards and controls. Curr Oncol 2011;18:e38-45. DESIGN: We collected all of the empirical claims made by Jose Pereira in "Legalizing euthanasia or assisted suicide: the illusion of safeguards and controls." We then collected all reference sources provided for those claims. We compared the claims with the sources (where sources were provided) and evaluated the level of support, if any, the sources provide for the claims. We also reviewed other available literature to assess the veracity of the empirical claims made in the paper. We then wrote the present paper using examples from the review. RESULTS: Pereira makes a number of factual statements without providing any sources. Pereira also makes a number of factual statements with sources, where the sources do not, in fact, provide support for the statements he made. Pereira also makes a number of false statements about the law and practice in jurisdictions that have legalized euthanasia or assisted suicide. CONCLUSIONS: Pereira's conclusions are not supported by the evidence he provided. His paper should not be given any credence in the public policy debate about the legal status of assisted suicide and euthanasia in Canada and around the world.
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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 | 0.035 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| 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".