Storying the Social: Opening Interpretations of Deafness, Disability, and Race in Accessibility Training Modules and Videoed Encounters
Bibliographic record
Abstract
This dissertation engages stories produced by, within, and for institutions of policing, in particular institutions of police, revealing different possibilities for how we might understand deafness, disability, race, and policing differently. It explores and analyses a set of cultural artefacts: (a) videos of encounters between d/Deaf people and police officers occurring in North America, and (b) accessibility training modules from the AccessForward program developed in 2012 by Curriculum Services Canada. Drawing on the work of the early Dorothy Smith and Thomas King, I develop my method of storying the social. Interpretive disability studies (specifically blindness as perception), Black methodologies (specifically counterstorytelling), and Indigenous epistemologies frame how I proceed, shaping my desire to unfold another approach to interpreting the human and the world of police training. In storying the social, a slow and poetic attention to the videos and modules reveals how linear patterns of storytelling obliterate signs of interpretation, producing problem-characters and watchable-types in relationship to a juridical discourse that authorizes police officers, specifically in Canada and the United States, as necessary enforcers of racial capitalist and settler colonial rule. Approaching our everyday lives as narratively-mediated appears deafness, disability, and race as interpretive scenes always already determined by routine protocols of accessibility training and police officer conduct. Foremost concerned with making and uncovering connections, storying the social is a transformative and abolitionist act of sociological inquiry that, by way of wonder and pause, opens what otherwise remains closed interpretations of deafness, disability and race. This way of proceeding challenges a dominant human imaginary that aspires to manage, define, extract, and dominate. Instead of presenting improved training programs as solutions to police violence, I argue for noticing the relations that we are already part of, such that a world that stories disability as a problem and institutions of police as problem-solvers becomes our object of perception.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".