Representations of Autism in Ontario Newsroom: A Critical Content Analysis of Online Government Press Releases, Media Advisories, and Bulletins
Bibliographic record
Abstract
In Ontario, Canada, autism has become widely politicized. In the last 20 years, instances of personal and organizational advocacy developed into wider-scale policy and programs. Government press releases indicate Ontario’s developing response to autism as a social policy issue, while reflecting societal perceptions and priorities surrounding autism. Informed by Critical Disability Studies and Critical Autism Studies, this article uses a content analysis to explore the manifest and latent priorities of Ontario’s provincial government displayed in press releases between 2001-2019 accessed through the Ontario Newsroom, an online repository of press releases and media advisories that features different initiatives published by the government of Ontario. Press releases were selected based on the search term “autism” and analyzed in two steps. First, this article presents the most frequently used words in press release headlines. Second, key themes within press releases are explored. Press releases emphasize the stories of non-autistic people, altruists, positivists, treatment-seekers, autistic children, and normative families. What is left out is a social representation of autism. Prominent themes display ableist perceptions of autism, reproducing power imbalances and inequity based on disability and family status. These findings reveal government objectives and priorities, reflecting broader societal perceptions of autism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".