On the right to accommodation for Canadians with disabilities: space, access, and identity during the COVID-19 pandemic
Bibliographic record
Abstract

 
 
 In this article, I explore the societal reluctance to accommodate and include persons with illnesses/disabilities, which has rendered them “second-class” citizens. This reluctance exists despite several pieces of legislation whose goal is to create an inclusive and accepting social as well as physical environment across Canada. In October 2020, the Ontario government introduced a mask mandate as a non-medical procedure to limit the spread of COVID-19. I argue that this mandate has further reduced civil society’s willingness to accommodate those who are unable to wear a mask due to their disability or medical condition, especially when their illness or disability is not visibly discernible. By making use of the concept of “state of exception” developed by Giorgio Agamben, and the biopower/biopolitics paradigm introduced by Michel Foucault, I attempt to examine what the mask mandate means for persons with disabilities as well as for society at large. My investigation is an effort to uncover why we are finding ourselves in a situation of inaccessibility and exclusion at this moment in time, despite the widespread rhetoric of unity and support for each other throughout the pandemic. Through a reading of Agamben, I aim to uncover why persons with disabilities have been, once again, considered justifiable collateral damage on the altar of necessity (in this case, the necessity to fight COVID- 19 at all costs).
 
 
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.047 | 0.034 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".