Bioethics Symposium: A scientist's guide to approaching bioethics1
Bibliographic record
Abstract
The Bioethics Symposium titled “A scientist's guide to approaching bioethics” was held at the joint annual meeting of the American Society of Animal Science, American Dairy Science Association, and the Canadian Society of Animal Science in Montreal, Quebec, Canada, July 12 to 16, 2009. The purpose of the symposium was to help animal scientists come to terms with the importance of grappling with bioethical issues in animal agriculture and to provide a practical approach to logically identify, discuss, and evaluate ethical issues they encounter. The presentation by Stricklin (2009) set the stage for the symposium by making the case that animal agriculture must come to grips with the public sentiment that considers animals as “subjects of a life” and worthy of consideration and care. However, this same public wishes to continue using animal products and, therefore, is relying on animal scientists and producers to develop ethics of care and assurance schemes that provide the animals we use for human purposes with a good quality of life. Addressing the ethical implications of our treatment of animals is ultimately compatible with the goals of animal scientists and producers because these actions promote animal agriculture systems that are socially sustainable.
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 | 0.043 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.020 | 0.035 |
| Insufficient payload (model declined to judge) | 0.018 | 0.020 |
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".