Bibliographic record
Abstract
JAVMA, Vol 233, No. 6, September 15, 2008 T often, veterinarians decline opportunities to discuss animal rights. They sometimes state that they support animal welfare, but they often avoid engaging the subject of animal rights. As a result, important questions regarding the appropriate and inappropriate use of animals are addressed by individuals who are frequently poorly informed in these matters. The public believes that veterinarians are experts on animal welfare and animal rights. A veterinarian’s formal education and experience qualify him or her to provide guidance in these areas. However, when the public seeks direction from veterinarians on matters of animal rights, the responses they receive are often vague or nonsubstantive. Some of the confusion that infects any discussion on animal welfare and animal rights results from a failure to focus on what does and does not impact the welfare of farm animals. Discussions on animal rights are often sidetracked onto issues such as vegetarianism, the urban consumer, farm size, organic agriculture, and other tangential subjects. Discussions should focus on the history of animal domestication with an emphasis on the use of the correct terminology to build well-constructed arguments. In this commentary, we distinguish between those areas germane to discussions on animal welfare and animal rights from those that are of an extraneous nature.
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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.034 | 0.030 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".