Animals in the Public Debate: Welfare, Rights, and Conservationism in India
Bibliographic record
Abstract
This paper proposes a survey of the many ways in which people look at and deal with animals in contemporary India. On the basis of ethnographic research and of multiple written sources (judgments, newspapers, websites, legal files, activist pamphlets, etc.), I present some of the actors involved in the animal debate—animal activists, environmental lawyers, judges, and hunter-conservationists—who adopt different, though sometimes interconnected, approaches to animals. Some of them look at animals as victims that need to be rescued and treated in the field, others fight for animals in Parliament or in Court so that they can be entitled to certain rights, others are concerned with the issue of species survival, where the interest of the group prevails on the protection of individual animals. In the context of a predominantly secularist background of the people engaged in such debates, I also examine the role that religion may, in certain cases, play for some of them: whether as a way of constructing a Hindu or Buddhist cultural or political identity, or as a strategic argument in a legal battle in order to obtain public attention. Lastly, I raise the question of the role played by animals themselves in these different situations—as intellectual principles to be fought for (or to be voiced) in their absence, or as real individuals to interact with and whose encounter may produce different kinds of sometimes conflicting emotions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".