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Record W1501555987 · doi:10.7202/1062367ar

Misfits in the Breach: Between Ecology and Economy in Helen Humphreys’s Wild Dogs

2019· article· en· W1501555987 on OpenAlexvenueno aff
Jessica Carey

Bibliographic record

VenueStudies in Canadian Literature · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsnot available
Fundersnot available
KeywordsBiopowerNeoliberalism (international relations)NarrativeIdeologyEcologySociologyEnvironmental ethicsPolitical economyPolitical scienceLawPoliticsBiologyPhilosophy

Abstract

fetched live from OpenAlex

This essay examines Helen Humphreys’s 2004 novel Wild Dogs, arguing that the narrative offers resistant responses to the seamless models of ecology and economy that are currently articulated by neoliberal culture. The often difficult lives of the canine and human misfits that populate the novel, alongside their sometimes unexpected actions and decisions, call attention to the inadequacy of ecological and economic narratives that would promise full and perfect, if cutthroat, functionality. The novel not only illustrates the socioeconomic and epistemic ill effects of a zero-sum neoliberal ideology of economic efficiency, but perhaps more importantly for situating neoliberalism within an ecocritical frame, the novel also interrogates the ecological dog-eat-dog story of “nature” that so often serves as the alibi for today’s spiralling and violent economic designations of biopolitical disposability. Both dogs and humans in Wild Dogs embody rankling remainders of the common-sense predator-prey binary; in the process, they initiate forms of care and relationship unaccounted for by the speculative presumptions of neoliberal biopolitics.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.982

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.233
Teacher spread0.214 · 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 designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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