Bibliographic record
Abstract
A prominent animal rights activist in New Delhi, explaining her relentlessness on behalf of animals, said to me the following: “I only wish there were a slaughterhouse next door. To witness that violence, to hear those screams . . . I would never be able to rest.” She was not alone among animal welfare activists in India in linking the witnessing of violence against an animal to the creation of a profound bond that demanded from her a life of responsibility. I argue in this article that this moment of witnessing constitutes an intimate event in tethering human to nonhuman, expanding ordinary understandings of the self and its possible social relations, potentially blowing the conceit of humanity apart. But I also consider another reading, which is how this act of intimacy exacerbates the species divide as the witness hyper-embodies herself as human, “giving voice” for the animal other which cannot speak. Throughout the article, I consider how posthumanist perspectives might trouble both these interpretations and ask what it would it mean to take seriously the animal activist’s “becoming animal.”
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.066 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".