Mourning the Nightingale’s Song: The Audibility of Networked Performances in Protests and Funerals of the Arab Revolutions
Bibliographic record
Abstract
Given the salient role of embodied tactics in contemporary networked protests in performance, in this essay I listen for how the embodied sonic praxis of protests during the Arab revolutions translates into the audio, visual, and text modalities of digital media. I propose audibility, or the appearance and perceptibility of sound objects, as that which translates the “live” sound that occurs in physical spaces into representational spaces, and, in so doing, alters the temporality and spatiality of the sonic experience. Interrogating who and what are rendered audible as part of the political contestations that drive protest actions, I demonstrate how audibility is a technological condition, sensory force, and social process through which affective publics emerge in networked spaces. I begin with social media posts from the first months of non-violent protest actions in 2011, in Egypt and Syria, analyzing the translation of sonic objects into written texts that narrativize the subjects and spaces of the Arab revolutions. I then shift to the sonic praxis of revolutionary mourning in a discussion of the audibility of the crowd in footage of protest funerals that reclaimed martyrs of the Syrian revolution in 2018 and 2019, interrogating how the sounds of the crowd enable the mythologization of the martyrs’ bodies and help mobilize the cause for which they died. Both approaches to audibility – as expressing voice and documenting sounds – underscore how audibility, I argue, is crucial for understanding the affect-rich intensities that drive networked protest performances, and that forge political possibilities as imaginable, sensible, and perceptible.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".