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Record W7020714405

The mind is all the animals it has attended: limitrophy and porous borders in the poetry of Robert Bringhurst

2024· article· en· W7020714405 on OpenAlexaboutno aff

Bibliographic record

VenueUniversidad de Córdoba Insitutional Repository (Universidad de Córdoba) · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAmerican History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryAnthropocentrismEmbodied cognitionSentienceCamouflageMateriality (auditing)PosthumanEcocriticismSubject (documents)Polyphony
DOInot available

Abstract

fetched live from OpenAlex

As an ecopoet intellectually alert to the conundrums posed by the Anthropocene, Canadian poet-philosopher Robert Bringhurst explores in his work the entanglements of the perceiving subject and the perceived world as mutually constitutive and coevolving entities in a vast material-semiotic continuum. Drawing on David Abram’s ecophilosophy, on the insights of the new materialisms about the vitality of matter, on posthumanist thought, and on Bringhurst’s meditations on ecology, polyphony and meaning, this article offers an ecocritical reading of “Sunday Morning,” a poem central to the poet’s oeuvre. At the heart of “Sunday Morning” is the firm conviction that poems are primarily born out of a sensuous immersion of an embodied self within a polyphonic Earth that is communicative and speaks languages other than human. Countering human exceptionalism, Bringhurst ends up by dispelling the hierarchy of being established by anthropocentrism in our Western mindset and blurring any clear-cut borders between the human and the nonhuman. His ecopoetics embraces ontological humility, as well as a biocentric view of nature where homo sapiens is just one more species amongst a myriad of other species on Earth.

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)0.001
metaresearch head score (Gemma)0.003
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: Other · Consensus signal: Other
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.019
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.229
Teacher spread0.216 · 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
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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