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Record W3211715183 · doi:10.21810/strm.v13i1.297

‘Creator gave us two ears and one mouth’

2021· article· en· W3211715183 on OpenAlexaffvenueabout
Lauren Knight

Bibliographic record

VenueStream Interdisciplinary Journal of Communication · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSoundscapeIndigenousEmbodied cognitionSound (geography)SociologyNarrativeField (mathematics)ConversationEcologyHistoryAestheticsVisual artsAcousticsEpistemologyCommunicationArtLiterature

Abstract

fetched live from OpenAlex

Acoustic ecology has served as a foundational theoretical field for many sound scholars to understand the soundscape as a signifier for environmental crisis. While sound theorists like R. Murray Schafer and those in the World Soundscape Project have developed ways in which to critically analyze environmental soundscapes, these methods have often excluded Indigenous narratives which offer complex understandings of sound through embodied experience. In this paper I employ a brief description of acoustic ecology, drawing attention to its benefits as a methodological approach to sonic ordering, while also demonstrating the possibilities for expansion of this field when examined in conversation with Canadian Indigenous perspectives and notable sonic activist movements. I address how Indigenous knowledge systems, futurisms, art, and activism can provide critical perspectives within the field of acoustic ecology, which lends well to understanding soundscapes of crisis. I identify a few case studies of sonic forward Indigenous environmental movements which include game design by Elizabeth LaPensée, Rebecca Belmore’s Wave Sound sculpture, and the Round Dance Revolution within the Idle No More movement. In sum, this paper works to bridge the work of acoustic ecology and Indigenous sonic movements to encourage a complex and nuanced relationship to sound, and to explore moments for understanding sonic intersections at the forefront of environmental crisis.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.962

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.078
GPT teacher head0.294
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2021
Admission routes3
Has abstractyes

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