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Record W4392350949 · doi:10.1111/aman.13965

What is “heard” at a pipeline hearing?: The gerrymandering of aurality in British Columbia, Canada

2024· article· en· W4392350949 on OpenAlexaboutno aff
Lee Veeraraghavan

Bibliographic record

VenueAmerican Anthropologist · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGerrymanderingHistoryPipeline (software)Media studiesPolitical scienceCriminologyLawSociologyEngineeringPoliticsDemocracy

Abstract

fetched live from OpenAlex

Abstract This article explores how sound technologies are deployed by government agencies to produce legitimacy in the struggle over oil pipelines in British Columbia, Canada. Activists seeking to stop the Northern Gateway and Trans Mountain pipelines have mobilized noise and silence as tactics of protest and refusal. For example, one thousand demonstrators make a cacophony outside a Vancouver hotel in protest of the Northern Gateway pipeline. Communications technology, though, is deployed here by the state to compress and control. In one of the hotel's small, impregnable conference rooms, public hearings over the pipeline are taking place—only the public is not allowed inside: the proceedings are being livestreamed to a hotel two kilometers away. On unceded Coast Salish territory, the legitimacy of pipeline hearings is also contested because the continued existence of Indigenous legal orders represents a challenge to the pipelines in question. Technological mediation makes it possible to satisfy one requirement of legitimacy: democratically granted representative power. The challenge to the legal system highlighted by the continued existence of the Indigenous, though, is managed through audile techniques deployed as anthropotechnologies. The implications for a politics of sound must be considered in light of sound's mediation, which is never politically neutral.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.995

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 studies0.0000.021
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.077
GPT teacher head0.276
Teacher spread0.200 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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