In Conversation With Erin Manning: A Refusal of Neurotypicality Through Attunements to Learning Otherwise
Bibliographic record
Abstract
This paper documents a conversation with Erin Manning in the first webinar of the series Doing Academica Differently: In conversation with Neuroatypicality. Drawing on her scholarship, teaching experience, as well as the more recent 3Ecologies project, Manning shows how systems serve to pathologize by framing difference from the angle of typicality and as a divergence from the norm. She argues, therefore, that it is necessary to move beyond the ontological presuppositions enacted by systems of whiteness/neurotypicality. She proposes that academic work must continue to remain open to the differential within difference, and value slow and convivial practices that texture qualities of existence as a mode rather than as gridded individual identities. By focusing on the crucial notion of value in higher education and how it might be reworked in experimental ways, Manning suggests ways of attuning for learning otherwise beyond a neurotypical frame.
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.023 |
| 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".