Representing Disability, D/deaf, and Mad Artists and Art in Journalism: Identifying Ableist Fault Lines and Promising Crip Practices of Representation
Bibliographic record
Abstract
This paper revisits the dynamic discussion about journalism’s role in representing and amplifying disability arts at the 2019 Cripping the Arts Symposium. Chronicling the dialogue of the “Representation” panel which included artists, arts and culture critics, journalists, and scholars, it reveals how arts and culture coverage contributes to the cultivation of disability, D/deaf, and mad art. Given that the relationship between journalism and disability communities continues to be fractured in Canada, speakers were invited to reflect on journalism and disability arts in relation to their own engagement with media as subjects, authors, and critics of disability arts reviews. The methods for presentation were cripped in multiple ways to provide the fullest access possible. The panel concluded with examples of ableist fault lines in representation practices where the disabled figure is an absent “ghost” in journalistic representation, warnings against journalistic reliance on traditional and objective narratives, and a call for artists to claim and write their own stories. Ultimately, disabled, D/deaf, and mad artists need both control over artistic endeavours and output and influence over representation. This article reconnects journalism and disability communities, ultimately demonstrating that representation is a critical, co-constitutive process that can become more aesthetically and politically oriented toward social justice in its focus on disability, D/deaf, and mad arts.
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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 | 0.054 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.035 | 0.086 |
| Scholarly communication | 0.048 | 0.024 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".