Revising Posthumanist Aesthetics in the Ethical Treatment of Nonhuman Animals
Bibliographic record
Abstract
Even with the increasing awareness of the importance of nonhuman animal life there remains an entrenched multitude of humanistic biases that hinder the development of the ways we see and treat nonhuman animals. This article examines Cary Wolfe’s posthumanist approach, which seeks to bring about a more inclusive nonhuman animal ethics by de-privileging the human species, and in doing so, identifies an impeding factor for the practical application of his proposal. Wolfe’s proposal for engaging a de-hierarchized sensorium requires the supplementation of a more relentless interrogation of human sight, specifically, the interrogation of the biases that come with human sight. In other words, this article identifies a humanist bias unaccounted for by Wolfe, the preference for the aesthetically pleasing, which impedes the possibility of realizing a more inclusive ethical framework towards nonhuman animals. The human aesthetic preference for beautiful, entertaining, and powerful animals does violence to animal species lacking these characteristics by excluding them from public purview, and in turn, from the support required to keep many of these species from extinction. In addition, a preliminary prescription is offered which argues for the paradoxical use of the humanist aesthetic bias against ourselves for ourselves, so as to open up humanity’s purview in hopes of a more inclusive ethics to come. In subjecting ourselves to such a manipulative attack we engage in a Derridean autoimmune process which opens humanity up to the nonhuman other by employing a posthumanist conception of care.
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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 | 0.027 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.115 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".