Why Seeing Is Not Believing and Why Believing Is Seeing: On the Politics of Sight
Bibliographic record
Abstract
Social movements often appeal to the politics of sight, meaning that if people knew about a given injustice, political transformation would follow. Jasmine English and Bernardo Zacka articulate two central premises of the politics of sight: “(1) exposing morally repugnant practices will make us see them, (2) seeing such practices will stop us from acquiescing to them.” Considering the case of slaughterhouse workers, Timothy Pachirat and English and Zacka challenge the previous premises. This article complements their contributions by theorizing what I call Western conceptuality/language and the role this plays in forming our subjectivities not to recognize violence on the one hand, and to be sovereign masters over animals on the other. I conclude by discussing the political implications of these arguments for the politics of sight, including the role of concealment and exposure, and the conditions needed for humans to see animals in their full ethical weight.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| 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".