With Haraway and Beyond: Towards an Ecofeminist and Contextual Vegan Ethico-Politics
Bibliographic record
Abstract
Abstract Some ecofeminist scholars have argued that being a feminist entails being a contextual vegan. Donna Haraway has opposed this position and received extensive critique. Yet no one, to my knowledge, has systematically studied how Haraway's theory can enrich ecofeminist vegan literature. To this end, I first establish the method of analysis, and/or framework, I use to read Haraway's work, what I call, interconstitutionality. Next, I delineate the limitations of Haraway's thinking insofar as it assumes a position of human dominion over animals. I then explore some aspects of Haraway's theory that can enrich ecofeminist vegan scholarship and provide insights to go beyond the limits of Haraway's corpus regarding: (1) the entanglements and embodied vulnerabilities that constitute human and non-human animals; (2) the agency of animals and the importance of curiosity and respect in leading just lives with other than human animals; (3) the ethical relevance of otherness, difference, and vulnerability at multiple scales: subject, community/herd, species, and cross-species (e.g., there are shared vulnerabilities between beings who are pregnant regardless of the species they belong to); and (4) the unavoidable violence that human existence entails. The text closes by affirming an ecofeminist non-anthropocentric vegan ontology and ethico-politics that aspires to overcome human dominion over animals.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.056 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".