‘It's a wide cluster of noise’: experiencing and describing information from environmental sounds
Bibliographic record
Abstract
Introduction. Uses and applications of environmental sound recording are expanding rapidly, shaped by demands for understanding and documenting changing climates and resulting in the generation of massive quantities of data. Situations around the creation and processing of these recordings are complex, suggesting numerous information-related challenges. However, little information practices research has directly engaged with sounds or sound recording. Method. Extending information research around sounds and embodied/sensory experiences, this qualitative study involved data generated from semi-structured interviews, participant observation and discourse materials. Participants were researchers working with environmental sound recording from various fields. Analysis. Guided by situational analysis, an extension of grounded theory, analysis involved iterative coding, memo-writing and analytic mapping techniques. Results. Preliminary findings are presented in two themes: noise, as concept and object, is constituted through participants’ situated information practices; and identification and description of sounds is tied to subjective/embodied experiences and ways of knowing. Conclusions. The ways situated knowledge and experiences shape how information from environmental sounds is created, sought and shared blur boundaries between signal-noise and between information activities. Embodied engagements with and descriptions of environmental sounds suggest the complexities of understanding related information practices and highlight the various relationships involved in knowledge production through environmental sounds.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".