Ethical vegetarianism and veganism
Bibliographic record
Abstract
The protest against meat eating may turn out to be one of the most significant movements of our age. In terms of our relations with animals, it is difficult to think of a more urgent moral problem than the fate of billions of animals killed every year for human consumption. This book argues that vegetarians and vegans are not only protestors, but also moral pioneers. It provides 25 chapters which stimulate further thought, exchange, and reflection on the morality of eating meat. A rich array of philosophical, religious, historical, cultural, and practical approaches challenge our assumptions about animals and how we should relate to them. This book provides global perspectives with insights from 11 countries: US, UK, Germany, France, Belgium, Israel, Austria, the Netherlands, Canada, South Africa, and Sweden. Focusing on food consumption practices, it critically foregrounds and unpacks key ethical rationales that underpin vegetarian and vegan lifestyles. It invites us to revisit our relations with animals as food, and as subjects of exploitation, suggesting that there are substantial moral, economic, and environmental reasons for changing our habits. This timely contribution, edited by two of the leading experts within the field, offers a rich array of interdisciplinary insights on what ethical vegetarianism and veganism means. It will be of great interest to those studying and researching in the fields of animal geography and animal-studies, sociology, food studies and consumption, environmental studies, and cultural studies. This book will be of great appeal to animal protectionists, environmentalists, and humanitarians
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".