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Record W1574033567

Storytelling Morality: Ecofeminism, Agrarianism, and Pigs in the Field

2014· article· en· W1574033567 on OpenAlexvenueno aff
Lissy Goralnik, Laurie Thorp, D. W. Rozeboom, Paul Β. Thompson

Bibliographic record

VenueThe Trumpeter · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsEcofeminismEnvironmental ethicsAgrarianismSociologyContext (archaeology)Agrarian societyMoralityNarrativeEnvironmentalismSocial sciencePolitical scienceLawAgricultureEcologyGeographyArtPhilosophyPoliticsLiterature
DOInot available

Abstract

fetched live from OpenAlex

Understanding our relationships with and obligations to the natural world through the labor and practice of food production is central to our development as moral beings and environmental citizens. Both ecofeminism and agrarianism—in their overlap and distance—can offer ideas about how best to express our environmental and citizenship ethics through the everyday act of growing, eating, and engaging with food. Raymond Anthony (2009) reminds us that a narrative ethics approach—embraced by both ecofeminism and agrarianism as a meaningful source of ethical wisdom—when applied to animal agriculture helps to build a more inclusive moral community. But Anthony cautions that the predominant agricultural storyline is made up of incompatible camps. He proposes a new story for agriculture, one that offers reconciliation or revitalization. In the spirit of this revitalization, we offer two stories of our material practice of raising pigs on an educational organic farm to illuminate what we see as important ethical, social, and environmental context for our new agricultural narrative. Through these stories we aim to give context to the theoretical ecofeminist and agrarian dialogue about ethics rooted in the land, so we might better understand what appropriate relationships with nonhuman others and natural systems might look like in practice.

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)0.001
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 score0.593
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.027
GPT teacher head0.314
Teacher spread0.287 · 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
Published2014
Admission routes1
Has abstractyes

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