Bibliographic record
Abstract
From call-and-response chants to the noise of pots and pans, protests are often defined by their sounds. In this book, Justin Eckstein argues that this is not merely the result of catchy slogans; it is due to sound’s ability to hold those in power accountable. Sound Tactics highlights how, in a world grappling with the uncertainty of emergent digital practices, social movements utilize the rhetorical power of sound. Eckstein uses the waveform as a metaphor for the persuasive potential of sound. Examining the case studies of the March for Our Lives protest, Howard University’s #HUResist movement, and the Casseroles protest in Montreal, Eckstein demonstrates how changes to the immediacy, intensity, and immersiveness of sound can affect the power of an argument. The collective use of sound in these case studies conveys the unity of the protesters in their demand for change and underlines the strength of their argument to those in power. More than just the written word spoken aloud, sound has unique layers of added meaning—it can convey length of time, demand attention, and signal disapproval. Eckstein’s study unpacks those layers for scholars and students as well as activists interested in deploying sound for change.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 0.025 |
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".