Unmuted: The racial politics of silent classrooms
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
Instructional resources often assume that students learn best when they have access to a quiet environment. This article interrogates silence’s presumed objectivity and innocuousness as the sonic backdrop for schooling. I argue that norms and expectations around silence in schools in the United States (US) inscribe a sonic color line. Such standards codify white, middle-class ways of sounding as an indicator of rationality. Simultaneously, they construct other ways of being sonically, particularly those traditionally associated with Black cultural norms, as generally unfit for school. The sanctioning of silent comportment in schools likely affects the academic achievement and sense of belonging of students whose sonic cultures differ from the schools’.I illustrate my argument with examples from classroom management resources published between 2001 and 2021. While silence’s role in constructing raced, gendered, and classed subjectivities prevails across school subjects, I focus specifically on materials for music educators. This school subject emphasizes sound production and reception, which makes its resources particularly explicit about sound management. I conducted a close reading of the materials informed by Foucault’s (Citation1980, 1978/1991) approach to the analysis of discourses, paying close attention to how silence-related norms and expectations shape students’ academic and ontological horizons.By mapping out silence’s role in producing a racial color line, this article underscores the central role that anti-Blackness continues to play in US schools nearly 70 years after school segregation was ruled unconstitutional.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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