Bibliographic record
Abstract
In our complex, consumerist societies, the intricacy of personal interactions and the number of goods and products available often prevents us from direct knowledge of what lies ‘behind’ food behaviors, ingredients, and the origins of the modern food and agriculture supply chain. Over the last decade or so, scholars, lawyers and engaged lay vegans have had many discussions about vegan rights and discrimination as issues intrinsic to animal rights, but the final frontier remains intact: the direct concerns of other animals. To give effect to the rights of animals, we must recognize and defend the human right—or duty, as many uphold-- to care about them. Including contributors from Australia, the United States, Germany, Italy, France, Canada, Portugal, and the United Kingdom, this book explores the rights of vegans and how vegans can be protected from discrimination. Using an international socio-legal lens, the contributors discuss constitutional issues, vegan legal cases, the concept of protection for vegan ‘belief’ in human rights and equality law, the legal requirement to provide vegan food, animal agriculture and plant-based, vegan food in the context of the human right to food, and the rights of vegans in education and in health care. This book will be of interest to practicing lawyers, legal and critical legal scholars, scholars of vegan, and critical animal studies, and commentors on socio-political issues alike.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".