Sonifying for Public Engagement: A Context-Based Model for Socially Relevant Data
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.
Bibliographic record
Abstract
In this paper we discuss the possibility for designing sonification as a tool for public engagement for socially relevant data. We do so through a case study of a specific sonification model and the results of a participatory focus group discussing ours and similar sonifications of “social” data. First we report on a unique and contextually-sensitive approach to sonification of a subset of climate data: urban air pollution for four Canadian cities. Similarly to other data-driven models for sonification and auditory display, this model details an approach to data parameter mappings, however we specifically consider the context of a public engagement initiative and reception by an “everyday” listener as a core principle informing our design. Further, we present an innovative model for FM index-driven sonification that rests on the notion of “harmonic identities” for each air pollution data parameter sonified, allowing us to sonify more datasets in a perceptually “economic” way. Finally, we discuss usability and design implications for sonifying socially relevant information based on user evaluation of our design and an open-ended discussion from two small-scale participatory focus groups. keywords:
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it