Using Community Service Learning as a Conduit to Decolonise Bachelor of Social Work Education
Bibliographic record
Abstract
Social work education and practice have been implicated in colonial violence against Black and Indigenous people in Canada. Notwithstanding, undergraduate students enter social work programmes ready to “help” service recipients. Schools of social work also continue to centre social work education around the notion of “helping” alongside other key activities such as advocacy and counselling. Regarding the intent, social work education and practice have and continue to perpetuate anti-Black racism, racism, and colonialism at the intersections of race, among some of the most vulnerable and systemically disadvantaged in society. This article demonstrates how to combine decolonising social work education and community service learning (CSL) to provide students an opportunity to critically and consciously work with community groups to meet the community’s needs. This reflective paper captures 1) the lessons learned and growth achieved among a group of undergraduate social work learners as they completed a CSL term project through a decolonised lens in partnership with Indigenous community members in Newfoundland and Labrador, Canada; and 2) the coaching and support that the teacher provided to the students to help them understand colonisation and their complicity as mostly white settler learners and future social work practitioners. The paper discusses the importance of CSL and decolonising social work education; then outlines the class’s context, process, and actions; next, through excerpts, CSL reflections are shared, and the paper concludes with a brief discussion.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".