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Record W4315642840 · doi:10.7202/1095383ar

The Language of Ecopoetry and the Transfer of Meaning

2023· article· en· W4315642840 on OpenAlexvenueno aff
Cassandra J. O'Loughlin

Bibliographic record

VenueThe Trumpeter · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsMeaning (existential)RealisationConsciousnessPerceptionTone (literature)PsychologyAestheticsAtmosphere (unit)MoodPoliticsBridge (graph theory)Cognitive psychologySocial psychologySociologyEpistemologyLinguisticsPolitical sciencePhilosophyLawGeographyPsychotherapist

Abstract

fetched live from OpenAlex

This article explores the properties of ecopoetry that have to do with the realisation that we are not merely external observers but active and intrinsic participants within the biosphere. The type of ecopoetics I am advocating takes a subjective stance to experience: it begins from within individual consciousness and is rooted in sensory perception. Reference to the world through this type of ecopoetry evokes a tone or mood, or “atmosphere” between environmental attributes and human experience that can solicit an emotional response. Ecopoetry can deliver meaning on a level beyond the direct connotations of the signs and symbols on the page. This has to do with “presence” as a phenomenological approach to the aesthetics of nature. Employing these concepts has the potential to bridge the gap between nature and politics, and influence attitudes towards living sustainably with the 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)0.005
metaresearch head score (Gemma)0.011
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: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.056
Scholarly communication0.0090.016
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.018
GPT teacher head0.305
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 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
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
Published2023
Admission routes1
Has abstractyes

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