Bibliographic record
Abstract
A mind-expanding exploration of the ethical bonds we share with the nonhuman Moral relationships saturate the living world, and the line between the human and nonhuman is blurrier than we might think. Animals, Robots, Gods provides a bold new vision of ethics defined less by the individual mind or society and more by our interactions with those around us, whether they are the pets we keep, the gods we believe in, or the machines we endow with life. Drawing on pioneering fieldwork around the globe by some of today’s leading researchers, acclaimed anthropologist Webb Keane invites us to expand our moral imagination. We learn about the ethical dilemmas of South Asian animal rights activists, Balinese cockfighters, cowboys, and Japanese robot fanciers. We meet a hunter in the Yukon who explains to a bear why it must come out of hibernation and generously give itself up to him, a cancer sufferer in Thailand who sees his tumor as a reincarnated ox, and a computer that persuades users to confess their anxieties as if they were patients on a psychiatrist’s couch. Through these and other stories, Keane challenges us to rethink our most basic ideas about who—and what—we deem worthy of moral consideration. Brimming with charm, wit, and insight, Animals, Robots, Gods reveals how centuries of conversations between us and nonhumans inform our conceptions of morality and will continue to guide us in the age of AI and beyond.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".