Bibliographic record
Abstract
Introduction to ethics and epidemics / John Balint, Martin Strosberg, Sean Philpott, Robert Baker -- The statue of security: human rights and post-9/11 epidemics / George J. Annas -- Quarantine and civil liberties / Paul J. Edelson -- The 2003 SARS outbreak in Canada: legal and ethical lessons about the use of quarantine / Nola M. Ries -- Can mandatory vaccination of health care professionals during an Influenza pandemic ever be justified? / Jaro Kotalik -- Medical ethics and epidemics: a historical perspective / Robert Baker -- Physician professionalism and preparing for epidemics: challenges and opportunities / Matthew K. Wynia, Jacob F. Kurlander, Shane K. Green -- Doctors, duties, and dangers: the reasonable physician and contagious populations / Chalmers C. Clark -- Ethically justified limits on the obligations of physicians and health care organizations in response to epidemics and bioterrorism / Laurence B. McCullough -- Ethics and epidemics in the developing world: the case of AIDS in Africa: treatment challenges / Angela Wasunna, Daniel W. Fitzgerald -- Under what conditions is clinical research in developing countries exploitative? a framework for assessing exploitation in mutually advantageous transactions / Angela Ballantyne -- A winter's tale: bioethics confronts Avian Flu / Laurie Zoloth, Stephen Zoloth
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".