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Record W4413014493 · doi:10.21810/aer.v1i1.5365

Embodied Listening Practices and Ruderal Ecologies

2023· article· en· W4413014493 on OpenAlexafffundabout
Lindsey French, Alex Young

Bibliographic record

VenueAcoustic Ecology Review · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsUniversity of Regina
FundersUniversity of Regina
KeywordsEmbodied cognitionRuderal speciesActive listeningCommunicationSociologyEcologyComputer scienceBiologyArtificial intelligence

Abstract

fetched live from OpenAlex

We propose embodied multisensory listening as a methodology of engaging with, and listening to, the complex multispecies relations of ruderal ecologies via our collaborative project in which we followed the path of the Line 3 petroleum oil sands pipeline from Edmonton, Alberta to Superior, Wisconsin in August 2022, enacting close engagement with the ruderal plant species that grow atop the overturned earth of this intercontinental site of colonial extractive infrastructure. Ruderal ecologies refer to the more-than-human constellations of life that form in human modified environments that are not purely conditional to any form of human action: even as certain human groups affect change to the earth both geologically and climatically through networked systems of extraction and exchange that span the Earth through colonialism and capitalism. Working from Nigerian feminist theorist Oyeronke Oyewumi proposal of the term “world-sense,” xwélmexw artist, curator and writer Dylan Robinson’s calls for multi-sensory listening, and Hsuan Hsu’s work on of olfactory art to confront us materially with the realities of environmental risk, we consider, can a methodology of embodied listening allow us to confront the living legacies of the ongoing colonial project of extractivism and imagine shared ruderal futures from a position of listening?

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.184
GPT teacher head0.318
Teacher spread0.134 · 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; both teacher heads agree on what is shown here.

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 routes3
Has abstractyes

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