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Record W4403432577 · doi:10.1002/pra2.1193

Information Practices and the Production of Scientific Knowledge through Sound

2024· article· en· W4403432577 on OpenAlexafffundabout
Owen Stewart‐Robertson

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsProduction (economics)Sound (geography)Knowledge productionSound productionKnowledge managementComputer scienceAcousticsEconomicsPhysics

Abstract

fetched live from OpenAlex

ABSTRACT Environmental sound recording is increasingly vital to the production of scientific and ecological information and knowledge. While research applications have grown tremendously, there is a lack of empirical research around the practices and experiences of those responsible for creating and managing these recordings. This poster reports on initial findings of a qualitative research project that interviewed 30 academic and professional researchers from across Canada and the United States, exploring information activities and meaningful elements and actors involved in their interactions with environmental sound recording. From preliminary findings, two themes are discussed: sound and listening skills involved in the creation and analysis of environmental sound recordings are complex, multisensory information practices, and changing environments and technological affordances enable and constrain the seeking and sharing of information and resulting knowledge production.

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)0.017
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.006
Science and technology studies0.0080.032
Scholarly communication0.0200.012
Open science0.0020.011
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.054
GPT teacher head0.273
Teacher spread0.219 · 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.

Study designQualitative
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 routes3
Has abstractyes

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