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Record W3168196444 · doi:10.3917/etan.741.0082

Dialectical Sonorities: Carbon Footprints in Peter Culley’s The Climax Forest

2021· article· fr· W3168196444 on OpenAlexaboutno aff
Jonathan Skinner, Tom Crompton

Bibliographic record

VenueÉtudes anglaises · 2021
Typearticle
Languagefr
FieldArts and Humanities
TopicEcocriticism and Environmental Literature
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtEthnologySociology

Abstract

fetched live from OpenAlex

Cet article s’intéresse à l’œuvre de Peter Culley, écrivain de l’île de Vancouver, et explore la manière dont les productions poétiques écrites sur d’anciens sites d’extraction de matières premières rendent compte des différents types de dommages repérés à l’échelle locale et mondiale, et y répondent. En tant que forme, la poésie tend à se situer à la [semi-]périphérie des économies institutionnelles et créatives, et nous avançons que des œuvres telles que celle de Culley sont souvent négligées alors qu’elles peuvent contribuer à l’étude d’une modernité inégale et combinée et de ses frontières écologiques. La trilogie Hammertown de Culley invite à des lectures collaboratives et collocatives par le biais de différents médias, et notre article répond à cette invitation en proposant une méthodologie participative. En nous appuyant sur le concept de « sonorité dialectique » de Mirko M. Hall, nous montrons que l’identification de la « place réelle » occupée par le pétro-capitalisme ne suppose pas tant de nouvelles stratégies narratives que de nouvelles techniques acoustiques. Notre quête sonore d’un autre type d’avenir dans le présent remixé de la poésie de Culley nous conduit à deviner des paysages au détour d’images de déclin mais aussi d’ouvertures possibles, et à toucher l’histoire en tant qu’échappatoire à l’histoire : par ce biais nous restons à l’écoute des difficultés de notre présent endommagé.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.998

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 studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.218
Teacher spread0.201 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Theoretical or conceptual
Domainnot available
GenreEmpirical · Other

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
Published2021
Admission routes1
Has abstractyes

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