MétaCan
Menu
Back to cohort
Record W4403336842 · doi:10.1017/s1054204324000224

Attunement to the Great Near with Rebecca Belmore’s <i>Wave Sound</i>

2024· article· en· W4403336842 on OpenAlexaboutno aff
Georgia Phillips-Amos

Bibliographic record

VenueTDR/The Drama Review · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAttunementSound (geography)Sound waveAcousticsGeologyHistoryPhysicsMedicine

Abstract

fetched live from OpenAlex

In 2017, on the 150th anniversary of Canadian confederation, Anishinaabe-kwe performance artist Rebecca Belmore set a quartet of listening trumpets entitled Wave Sound on shorelines across Canada’s national parks. Her metallic instruments amplify the water’s frequencies by drawing them in close, inviting visitors to stop and listen to the found sounds. This technology operates on the scale of W.E.B. Du Bois’s rarely considered sci-fi “megascope,” inviting a new mode of listening to the vital and resilient voice of the lands and waters beyond narratives of settler colonialism.

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)0.002
metaresearch head score (Gemma)0.003
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.717
Threshold uncertainty score0.569

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.005
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.001

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 head0.022
GPT teacher head0.233
Teacher spread0.211 · 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
GenreOther

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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueTDR/The Drama ReviewSame topicSeismic Waves and AnalysisFrench-language works237,207