Veganism and animal welfare, scientific, ethical, and philosophical arguments
Bibliographic record
Abstract
The justification for this review article is to understand the position of vegans and those individuals who consume food of animal origin from an unbiased perspective but with a grounding in scientific evidence. This will provide people who eat meat with scientific and ethical arguments to defend their alimentary autonomy in the context of the moral conflict that has emerged in societies regarding the consumption of meat and animal products, which is criticized –sometimes even attacked– by activists, ovolactovegetarians, or vegetarians with alimentary habits that stress ethical and moral respect for animals. These individuals refuse to eat meat and animal products but sometimes show disrespect for those who do. In recent decades, veganism and vegetarianism have reached an apogee in some western societies where they are often considered a healthy option for humans that simultaneously fosters animal and environmental welfare. While those diets may provide numerous benefits, they can also entail health risks by failing to provide balance and necessary dietary supplements. Various researchers concur that they are not appropriate for pregnant women, children, or carnivorous or omnivorous pets. Our review of scientific articles in favor and against dietary regimens that lack protein of animal origin leads to the conclusion that these dietary changes, on their own, do not reduce animal suffering or the contamination generated by the meat, dairy, and poultry industries. Finally, it is important to consider that, despite the popular opinion that vegetarianism and veganism are healthy diet alternatives, the diet must be individualized and well-balanced according to each stage of their life cycle.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".