Funding Priorities: Autism and the Need for a More Balanced Research Agenda in Canada
Bibliographic record
Abstract
The public purse is responsible for funding almost all autism spectrum disorders (ASD) research in Canada (as per Canadian Institutes of Health Research [CIHR]) and for providing some of the existing services and supports for this population. In this article, we consider various reasons why Canada should be concerned to ensure a more equitable distribution of relevant public funding for ASD research than is currently the case to meet the express needs and interests of the diversity of autism stakeholders. As such, we report data to show that CIHR-supported ASD research from the period of 2000–2010 demonstrates a bias focussed on the aetiology of the condition revealing a disproportionate emphasis on only two (Biomedical and Clinical) out of the four research pillars avowed by CIHR, with a comparative lack of fiscal resources committed to Health Systems and Services and Population and Public Health research. We advance certain normative and prudential reasons for funding more Health Systems and Services and Population and Public Health ASD research in Canada. In our view, this would seem to follow from CIHR’s official mandate ‘as a flexible mechanism that will continually align health research funding with changes in the manner in which health problems and opportunities are identified, understood and addressed’.
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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 | 0.072 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.036 | 0.024 |
| Scholarly communication | 0.035 | 0.013 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".