Neuroqueer(ing) Noise: Beyond ‘Mere Inclusion’ in a Neurodiverse Early Childhood Classroom
Bibliographic record
Abstract
Inclusion, as it is understood in a British education context, usually refers to the integration of children with dis/abilities into a mainstream school. However, rather than transform the school, inclusion often seeks to rehabilitate—to tune-up—the ‘divergent’ child’s noisy tendencies, making them more easily included. Music and the arts more broadly have long been instrumentalized as one way of achieving this transformation, relying on the assumption that there is something already inherently opposed to music—out-of-tune, or noisy—about that child. In this article, I think and compose with Neuroqueer(ing) Noise, a music research-creation project conducted in an early childhood classroom. I draw from affect and neuroqueer theories to consider how the instrumentalization of music as a way to include autistic children relies on the assumption that ‘they’ are already inherently unmusical. I consider how a deliberate attention to noise might help in unsettling ‘mere inclusion’: in effect, changing the mode we think-with in education, and opening us—researchers and educators—to momentarily say “No!” to ‘mere inclusion’. This article is of relevance to teachers working in early childhood classrooms, as well as to educational researchers interested in affect theories, crip-queer and neuroqueer theories, and neurodiversity, as well as sound- or arts-based research methods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".