Information from sound: exploring sounds and listening in information practices research
Bibliographic record
Abstract
Introduction. This conceptual paper discusses the possibilities for expanding research around sounds/listening and sound-related practices in information research to further understandings of embodied/sensory information practices and attend to a greater diversity of information experiences and ways of knowing. Method/Analysis. The growth of research related to broad conceptualisations of sound and listening and the use of information from sound in knowledge production across many fields is discussed. Some challenges faced by that research and gaps in existing sound-related information practices research are noted. Results. Sound research in other fields faces issues around the management, interpretation, and contextualisation of sound-related data, and little is understood about the practices of sound recordists. Some information practices-related research has highlighted the complexity of interactions with sounds and related technologies and explored interactions with oral and music information sources. However, experiences and perceptions around seeking, creating, and using information from sounds lack in-depth study. Conclusion. The value of further information practices research related to sound is suggested: to expand embodied/sensory information research, to engage with the broad range of sonic skills and experiences, to further holistic examinations of information interactions, and to address information-related problems and research gaps in sound-focused research from other fields.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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".