Bibliographic record
Abstract
This is the second edition of the textbook Bioethics in Canada. It is the most up to date bioethics textbook on the Canadian market. Twenty-nine of its 54 contributions are by Canadians. All the chapters carried over from the first edition are revised in full (especially the chapters on obligations to the global poor, on medical assistance in dying, and on public health). It comprises *new* chapters on emerging genetic technologies and on indigenous peoples' health. It contains *new* case studies focusing on ethical issues and problems of relevance to Canadians. From the Preface: This anthology is designed for those teaching bioethics in colleges and universities in Canada. It comprises articles from researchers exploring the main problems of bioethics from a diversity of perspectives and ethical traditions. It includes in particular articles by Canadian researchers who appear in anthologies less often than they should. The hope is that the reader will, as a result, better appreciate the rich reservoir of talent present among those working in bioethics in Canada and Canadian bioethicists working abroad. In addition, this volume intentionally aims to educate the reader about the policies and laws regulating the most important and pressing bioethical problems facing Canadians. The hope is that the reader will develop a nuanced view of the nature, importance, and impact of bioethics in Canada.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.101 | 0.026 |
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".