Autism, Equity, and How the Journal Came to Be
Bibliographic record
Abstract
Over the past year a pandemic has swept across the world and, unsurprisingly, revealed gross inequalities across all aspects of life. We saw this in the constant pandemic media coverage that overlooked the experiences of the disability community and, more specifically, the autism community, at least at first. Furthermore, let us not forget in the early days of the pandemic that in countries such as Italy, people without disabilities were prioritized for life-saving machines (Andrews et al., 2020; Lund & Ayers, 2020), contributing to a culture of fear for the one in five Canadians with a disability (Morris et al., 2018) about what would happen to them here. As COVID-19 reached Canadian shores we saw this pattern of inequity quickly replicated within our society. For instance, Canadians with developmental disabilities, such as autism, living in residential settings did not receive the same level of support as those living in different kinds of residences such as retirement residences (Abel & Lai, 2020). Likewise, the initial claims that only people with ‘preexisting conditions’ were at risk implied that those at risk were somehow less valuable to society. Nothing has highlighted the very real problem and extent of ableism within Canadian society as a whole more than these injustices arising from the COVID-19 pandemic, and this is what planted the seed for the Canadian Journal of Autism Equity (CJAE).
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.030 | 0.077 |
| Scholarly communication | 0.039 | 0.017 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".