Sonifying for Public Engagement: A Context-Based Model for Socially Relevant Data
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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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".