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Record W2009835094 · doi:10.5210/fm.v17i4.3808

Looking at archival sound: Enhancing the listening experience in a spoken word archive

2012· article· en· W2009835094 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueFirst Monday · 2012
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsConcordia University
Fundersnot available
KeywordsActive listeningVariety (cybernetics)PoetryReading (process)Context (archaeology)Computer scienceSpoken wordMultimediaWorld Wide WebLinguisticsHistoryPsychologyCommunicationArtArtificial intelligenceLiterature

Abstract

fetched live from OpenAlex

What helps researchers listen in deep and engaged ways to poetry that is delivered on the Web? This paper considers how visual aspects of Web–based archives for poetry recordings can enhance the listening experience for users by providing more context and clarification that can help users better understand and use the recordings. Drawing from studies in a variety of disciplines that demonstrate that much of our learning is multimodal, the SpokenWeb project in Montreal, Canada is using digitized live recordings of a Montreal poetry reading series from 1965–1972 featuring performances by major North American poets to investigate the features that will be the most conducive to scholarly engagement with recorded poetry recitation and performance. Visual features such as tethering audio playback with a written transcript, sound visualization and including videos and images are discussed as means to enhance the listening experience in a digital spoken word archive.

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.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.490

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.257
Teacher spread0.236 · 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