Looking at archival sound: enhancing the listening experience in audio archives
Bibliographic record
Abstract
What helps researchers listen in deep and engaged ways to sound recordings that are 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 \nconducive 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 audio archives. While visual features will be discussed from the perspective of the SpokenWeb poetry project, many of the issues and recommendations will apply more broadly to audio archives in general, including those with both spoken word and musical content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".