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.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".