Best Practices in Promoting Disability Inclusion Within Canadian Schools of Social Work
Bibliographic record
Abstract
The profession of social work has a long history of work with "clients" with disabilities, but unfortunately, this history often has not included strong advocacy for their rights and creating a place as colleagues within Schools of Social Work (Dunn, Hanes and MacDonald, 2003). From a critical disability perspective and a view of disability as being socially constructed, the profession and its educational institutions need to rethink their approach to students, faculty and staff with disabilities (May & Raske, 2005). Best practices in accessibility, accommodation and inclusivity will be explored within Canadian Schools of Social Work. Knowledge shared in this article was derived from a critical review of the literature, a survey of Schools of Social Work in Canada (Dunn, Hanes, Hardie, and MacDonald, 2006), and a National Best Practices conference (Dunn, Hanes, Hardie, Leslie, and MacDonald, J, 2004). Disability inclusion within Schools of Social Work is explored in five main areas: 1) recruitment and admissions; 2) accommodation; 3) curriculum; 4) field placements; and 5) retention, graduation and meaningful employment. While the specific focus is on social work education the principles and practices can be applied to other disciplines within the academy and beyond.
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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 | 0.033 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.054 | 0.016 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| 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".