Soundmapping as critical cartography: Engaging publics in listening to the environment
Bibliographic record
Abstract
There is a kind of growing new media practice of capturing and mapping sound and an emergent global community of listeners interested in engaging with sounds of the environment, urban space, habitats and biospheres. Between user-driven Instagramming our everyday audio-visual experiences and professionally curated sound installations, there is an emergent space and a global audience for listening to ‘soundmaps’ of local and global environments. Sometimes interlinked and sometimes disparate, these communities connect to wider communities of practice and (environmental) activism in the context of social media, new media production and participatory cultures. There are also growing research initiatives that take up soundmapping as a way of inquiring into pressing spatial, geo-political and cultural issues primarily in cities and also in the endangered wilds. Interest in sound in a variety of interdisciplinary fields has grown exponentially over the last few decades. This article will externalize and analyse the frames of several emergent communities and their organizing themes as nascent in new media culture, and social networks specifically, as they intersect with phonography, creative soundmaking and ‘citizen science. By pointing out normative logics embedded in the practice of soundmapping, I then work towards a language of critical soundmapping by way of three examples that I suggest function as alternative forms of representation of and communication about sound environments: (1) the curated initiative Cities and Memory, (2) the creative research project London Sound Survey and (3) the climate change project Biosphere Soundscapes.
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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 | 0.025 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.023 | 0.084 |
| Scholarly communication | 0.029 | 0.027 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.008 | 0.011 |
| 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".