A just recovery from the COVID-19 pandemic for people with disabilities: Descriptive analysis of Canadian survey data
Bibliographic record
Abstract
Emerging research shows that people with disabilities have experienced a worsening of economic and health status, housing conditions, social support, and personal safety during the COVID-19 pandemic. Policy responses to COVID-19 have neglected the needs of people with disabilities, prompting community organizations to advocate for a just recovery that centres disabled people. In 2021 the Disability Justice Network of Ontario (DJNO) conducted an online, national survey asking people with disabilities about their experiences during the COVID-19 pandemic and what their needs are for a just recovery. The survey collected quantitative and qualitative data in multiple domains, including respondent demographic information, just recovery/quality of life, education and labour force, social services and the justice system, housing, and health care. This report presents a descriptive analysis of DJNO’s survey results for Ontario respondents. The results of this survey revealed an urgent and large need for economic, social, housing, and health support for people with disabilities.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.024 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".