Considering Autistic Women and Girls in Public Policy: A Review of British Columbia
Bibliographic record
Abstract
Despite growing evidence that sex and gender shape how autism is experienced, diagnosed, and supported, autistic women and girls remain critically overlooked in British Columbia’s autism policies. This gap contrasts with Canada’s equity commitments and raises urgent questions about the inclusivity of current policy frameworks. This capstone examines how government-produced materials referring to autism policies, supports, and services in British Columbia from 2014 to 2024 consider autistic women and girls. It offers the first comprehensive assessment of British Columbia’s autism policies through a gendered lens. Guided by Gender-Based Analysis Plus (GBA+) and the Intersectionality-Based Policy Analysis (IBPA) framework, this research examines how policies recognize identity, incorporate inclusive data practices, and meaningfully engage with diverse stakeholders. Through a scoping review methodology guided by JBI and PRISMA-ScR standards, 60 government-produced materials were analyzed. Only 13 sources referenced sex or gender, typically through prevalence statistics. One-third mentioned other identity factors, most commonly Indigeneity. However,inclusive data practices and diverse stakeholder engagement were limited overall. This capstone concludes with 12 Calls to Action, highlighting three overall priorities: (1) improve disaggregated data and stakeholder engagement, (2) implement a provincial autism strategy and/or legislation, and (3) update the National Autism Strategy to include gender-based considerations. Adopting these measures would align British Columbia’s autism policy with federal equity commitments and international best practices, ensuring autistic women and girls are recognized 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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.035 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".