A “useful uselessness”: vegan geographies of bearing witness at the slaughterhouse gates
Bibliographic record
Abstract
Founded in Toronto in 2010, the SAVE Movement is a growing grassroots vegan activist network of nearly two hundred autonomous groups in over twenty countries. Central to its activism is bearing witness to nonhuman animals as they are transported into slaughterhouses; attempts to relieve suffering momentarily (with water); and bringing awareness to the plight of nonhuman exploitation and commodification through video footage, imagery, and testimony, with the goal of convincing others to practice veganism. Critics have suggested such activism is “useless” in that it does not “save” the unique animals in the trucks. This chapter responds by drawing attention to the effects of the heightened encounters and embodied contacts that take place in the charged spaces outside the slaughterhouse gates. While critiques of activism are necessary to understand its contribution to the goal of ending nonhuman exploitation, this chapter argues these acts of bearing witness are, rather, “useful” practices in troubling the power relations that the particular spatial orientation of the slaughterhouse sets in place. The vigils politicize the logic of human domination of nonhuman others on the verges (often literally) between everyday life and the industrial locations of nonhuman bodily appropriation. Employing autoethnography and participation with SAVE activists at vigils, this chapter interrogates concepts of ethical responsibility to propose the idea of a “useful uselessness” in relation to animal activism, noting how it opens out the otherwise highly-controlled and overlooked spaces of material entanglement between human and nonhuman at the intersection of slaughterhouse and public way.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".