Social Work Faculty Engagement in Social Policy Practice: A Quantitative Study of the Canadian Experience
Bibliographic record
Abstract
Abstract This article reports on a quantitative study of how Canadian social work educators engage in social policy practice. The first part of the article contextualises social policy in Canada, explains how social policy has been incorporated into Canadian social work education and concludes by posing the research question. The second part presents the study’s findings of how and to what extent Canadian social work academics engage with social policy including its development, analysis and implementation. Thirty-one educators representing seventeen of Canada’s forty schools of social work responded to a standard questionnaire used in a collection of cross-national studies. The Canadian responses were analysed to determine (1) the level and type of engagement in policy including the perceived impact on social policy and (2) the institutional and individual factors associated with their engagement. Some comparisons are made with other countries studies. The third part of the article provides overall concluding comments linking the first part with the findings of the second, speculates on the reasons for some of the findings and poses some ideas for future research. Finally, it laments the increased focus of Canadian social work on individual pathology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.036 | 0.017 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".