Bibliographic record
Abstract
Ontario overhauled their autism program in 2019 seeking to replace the flexible service-or funding-based 2018 Ontario Autism Program with a cash-based benefit called the Childhood Budgets program.Using Grounded Theory and Participatory Action Research, with a Structural Social Work and Critical Autism Studies lens, I used one-to-one interviews to ask four autistic adults in Ottawa, Ontario, their perspectives of autism funding and Applied Behaviour Analysis in Ontario.Participants reflected on how they identified with autism to frame their discussions in this research using prevalence rates of autism to exemplify a need to "demystify" and "de-monstrify" autism (Participant 'Tom,' A personal communication, Sept 25, 2019).Participants highlighted the importance of enjoying supports and services.They identified that supports could improve through increasing adult services and by teaching self-advocacy skills.Some participants did not trust the school system because of inadequate or inappropriate provision of support.Participants found that schools could improve by giving autistic students skills to "work in the real world" and de-instituting exclusionary practices (Participant 'James,' A personal communication, Oct 29, 2019).The lack of adequate funding, services, and supports for autism in Ontario may have increased some of the participants' use of medication.Participants acknowledged that medications could help and harm, but "can't really solve a problem."(Participant 'Tom,' A personal communication, Sept 25, 2019) Participants wanted inclusive policy-making opportunities for autistic people.All agreed that some of the costs for services should be covered, because paying can "sometimes make it look like the autistic child's a burden."(Participant 'Philip,' A personal communication, Oct 22, 2019) This research I wish to thank Bridget Liang, a PhD Candidate and co-founder of Autistics 4 Autistics and Autistiqueers, who acted as a paid reader of my ethics protocol during the summer of 2019: pushing me to consider how my research would not harm participants while promoting social change.Thank you to Christine Jenkins, co-author of Spectrum Women, who acted as a paid reader of Chapters 1 through 3 and providing rich resources and considerations with regards to semantics, history, and autistic selfadvocacy.I wish to thank my family who has supported me emotionally, mentally, financially, and academically.Thank you for appreciating me even with my need for routines, structure, and control.Thank you for always providing me with opportunities to learn and grow, for teaching me how to learn, and Mom, thank you for every time you cut out an 'autism article' in the newspaper and sent it to me.I wish to thank my community at Carleton Athletics, where I played out my special interests.You taught me new skills and to love learning, and challenged me to become engaged socially.Richard especially, identifying my special interest, taking the time to teach me to clean, jerk, and snatch, appreciating my early morning grumpiness and encouraging me to find time to take care of myself throughout the research process.v
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".