Applied Animal Ethics in Industrial Food Animal Production: Exploring the Role of the Veterinarian
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Industrial food animal production practices are efficient for producing large quantities of milk, meat, and eggs for a growing global population, but often result in the need to alter animals to fit a more restricted environment, as well as creating new animal welfare and health problems related to animal confinement in high densities. These practices and methods have become normalized, to the extent that veterinarians and others embedded in these industries rarely question the ethical challenges associated with raising animals in this fashion. Moral 'lock-in' is common with those working in food animal industries, as is the feeling that it is impossible to effect meaningful change. Animal welfare issues associated with the industrialization of food animal production are 'wicked problems' that require a multi- and transdisciplinary approach. We argue that veterinarians, as expert animal health and welfare advocates, should be critical stakeholders and leaders in discussions with producers and the food animal sector, to look for innovative solutions and technology that will address current and future global sustainability and food security needs. Solutions will necessarily be different in different countries and regions, but ethical issues associated with industrial food animal production practices are universal.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it