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Record W4308003895 · doi:10.52537/humanimalia.11190

Re-Animalizing Animal Farm

2022· article· en· W4308003895 on OpenAlexaff
John Drew

Bibliographic record

VenueHumanimalia · 2022
Typearticle
Languageen
FieldPsychology
TopicScience Education and Perceptions
Canadian institutionsWestern University
Fundersnot available
KeywordsAnthropocentrismHumanismDeconstruction (building)SociologyInterpretation (philosophy)ExceptionalismEnvironmental ethicsAllegoryHegemonyEpistemologyFrame (networking)AestheticsPhilosophyLiteraturePolitical scienceEcologyArtLawLinguisticsBiology

Abstract

fetched live from OpenAlex

Interpretations of George Orwell’s Animal Farm have been almost exclusively focused on anthropocentric allegory in the text and what I call the anthropo-allegorical interpretive frame. Given Animal Farm’s iconic and enduring status in English classrooms, I unpack this process, particularly how it is informed and perpetuated by the persistence of human exceptionalism rooted in the humanist literary tradition, and hegemonic approaches to education. I employ Derridean deconstruction to critique the humanist and educational legacies that inform the largely homogenized and de-animalized interpretation and pedagogical applications of Animal Farm. Then I argue for a new, hybridized reading — and teaching — that moves beyond the anthropocentric and toward a more-than-human interpretive and pedagogical orientation that speaks to the oppressions and challenges confronting multiple species, including, but not confined, to our own.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.036
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.153
GPT teacher head0.415
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2022
Admission routes1
Has abstractyes

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