Childism, Adultism, and Language Barriers in Equity, Diversity and Inclusion (EDI) Messaging: An Analysis of EDI Statements across Child and Youth Autism Centres, Clinics and Hospitals in Canada
Bibliographic record
Abstract
In the past three years, organizations in Canada have been asked to better address issues of equity, diversity, and inclusion (EDI). Organizations have been encouraged to self-reflexively look inward, to examine how institutional policies and practices serve EDI goals. EDI is increasingly regarded as a social justice issue. In the Canadian autism and autistic community, more attention is being devoted to EDI. However, to date EDI messaging has not been explored in the context of child and youth autism centers and hospitals. We conducted a scan of EDI messaging across child and youth Autism Spectrum Disorder (ASD) centres in Canada. We utilized a document analysis approach. We found major geographic disparities in EDI messaging with most EDI messaging originating from Ontario. Some ASD centers did not have EDI statements. EDI messaging was mainly directed toward adults, and in this way reflected discourses of childism and adultism. Despite Canada’s growing language diversity, EDI statements were mainly in English only and reflected a lack of engagement with other languages. Statements were Euro-centric. Vagueness in EDI statements, tokenistic EDI statements, and a lack of attention to intersectionality in EDI statements continue to be problems of a moderate scale. Finally, many ASD centers were reliant on broader institutions’ EDI statements and did not generate their own EDI statements. Suggestions to improve EDI messaging in the context of pediatric care – such as engaging children and families in the writing of EDI statements and taking accountability and responsibility to generate one’s own – are proposed.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".