Bibliographic record
Abstract
This book has had a long evolution and owes a debt to more people than I can mention here.First and foremost, I thank the participants in my research, who generously shared recollections of their experiences.Several have died, and I regret that I will never know what they would have thought of this account.The willingness of each participant to engage with the issue supported my belief that the events recounted here matter to the community, past and present.I hope my rendition of our collective journey merits the trust they put in me and opens a path for further exploration.My research is based in part on documents, experiences, and memories from the 1990s, when some of the research participants and I were active in Canada's breast cancer community.I owe the opportunity to conduct a formal study to my years as a fellow in the CIHR Training Program in Ethics of Health Research and Policy, jointly sponsored by the Department of Bioethics at Dalhousie University and the W. Maurice Young Centre for Applied Ethics at UBC.Through this program I became part of an extraordinary community of professors and research fellows grappling with contemporary ethical issues in medicine.Among those whose work inspired and informed my own were
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.517 | 0.281 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".