Deafness, Disability, and Neurodiversity in Architecture: An introduction
Bibliographic record
Abstract
Ιn her 2022 review of David Gissen’s The Architecture of Disability for the Journal of Architectural Education (JAE), architectural and urban historian Wanda Katja Liebermann notes: ‘That this is JAE’s first review of a book on disability confirms architecture’s lag in treating impairment as fundamental to design discourse’.1 This poignant comment can be extended beyond JAE. Despite having sporadically featured scholarship that grapples with bodily diversity and impairment,2 the longest-standing journals of architecture with renowned presence in the field have not yet consistently devoted thematic issues centred on these questions. arq does this now, and Liebermann’s words have prompted us, as curators of the issue, to reflect on our responsibility and the positionality of this special issue on ‘Deafness, Disability, and Neurodiversity in Architecture’ within the wider ecology of architectural publishing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".