Veganism as Affirmative Biopolitics: Moving Towards a Posthumanist Ethics?
Bibliographic record
Abstract
This article addresses tensions within the emerging field of animal studies, which have arisen in the process of trying to craft an ethics that is not grounded in humanist rights-frameworks, by--firstly--mapping how these debates are manifested and--secondly--positing Cary Wolfe’s concept of "affirmative biopolitics" as means of overcoming these conceptual rifts. Building on work that attributes these tensions to the influence of posthumanism (Weisberg; Pedersen; Giraud), it argues that the embrace of posthumanist thought has marginalised critique by (misleadingly) framing perspectives such as ecofeminism and critical animal studies as irredeemably humanist (thus of no use in forging a non-anthropocentric ethics). To counter this marginalisation, Wolfe’s recent work on biopolitics is used to create a much-needed conversation between these perspectives. Debates surrounding veganism provide a route into instigating this dialogue, due to it being a contested practice that crystallises the differences between "mainstream" and critical animal studies. This framing of veganism not as a totalising practice but as a form of "affirmative biopolitics," however, is not solely intended to highlight affinities between apparently antagonistic perspectives, but offered as a contribution to broader debates about how a "posthumanist ethics" could be enacted in practice.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.072 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".