Soundscapes of Resistance: Amplifying social justice activism and aural counterpublics through field recording-based sound practices
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
This article examines creative sound practitioners who audibly convey social justice commentary through their use of environmental soundscapes as source material. I discuss how micro-watt radio pioneer Mbanna Kantako, electronic music artist Muqata’a and audio activist Christopher DeLaurenti work with field recordings to produce subversive counter-narratives against news media and state discourses. I outline three specific sound projects as case studies: Kantako’s aural counter-surveillance of police encounters within the predominantly poor and Black neighbourhood of Springfield, Illinois; Muqata’a’s album Inkanakuntu (2018) composed using field recordings of Ramallah, West Bank; and DeLaurenti’s radio piece Fit the Description (2015) that incorporates field recordings of the protests following the 2014 police killing of Michael Brown in Ferguson, Missouri. I argue that composing with soundscapes of contested urban spaces can function as sonic activism that confronts the oppressive soundscapes of systemic racism. The case studies are examined through the following common themes: 1) the use of what I term aural counterpublics to amplify marginalised voices and soundscapes of resistance, and 2) the radical re-appropriation of microphones and oppressive police and military audio technologies as a means of ‘speaking back’ to systems of power. Finally, I suggest how these case studies convey the need for intersectional and decolonised approaches to soundscape studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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