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Record W4394855370 · doi:10.1017/hyp.2023.108

With Haraway and Beyond: Towards an Ecofeminist and Contextual Vegan Ethico-Politics

2024· article· en· W4394855370 on OpenAlexaff
Pablo P. Castelló

Bibliographic record

VenueHypatia · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsQueen's University
Fundersnot available
KeywordsAnthropocentrismSociologyEpistemologyEnvironmental ethicsNon-humanPoliticsPosthumanScholarshipAgency (philosophy)EcofeminismPhilosophyLawPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.338
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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