Attuning to<i>In‐the‐Red Frequencies</i>with/in Readers Workshop
Bibliographic record
Abstract
Abstract In this article, we ‘think with’ the theoretical concepts of flow, rupture, layering, and sampling to affectively attune to ‘in‐the‐red frequencies’ flowing across/with‐in a New York City primary classroom—that is, alternative sonic frequencies that trouble and refuse hegemonic literacy practices. These hip‐hop concepts theorise affect in relation to Black intellectual frameworks for moving, feeling, and sounding. Such frameworks honour philosophical practices emerging from Black people's lived experiences—practices that, historically, have been perceptually coded out of legibility by white supremacist institutions. Ultimately, we argue that thinking with flow↔rupture↔layering↔sampling enables more equitable practices that push literacies ‘into the red,’ namely, by respecting multiple perspectives, histories, and truths; accounting for power, privilege, positioning, and complicity; and highlighting ‘otherwise’ social worlds not predicated on hegemonic whiteness, anti‐blackness, and socio‐political violence.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".