The Call to Decolonise: Social Work’s Challenge for Working with Indigenous Peoples
Bibliographic record
Abstract
Abstract Using Canada as an example, social work must not only address its historical and current role in the colonisation and assimilation efforts aimed at Indigenous people, but also deconstruct its practices. Social work theory, methodology and practice parameters have been built on Eurocentric definitions and understandings. Indigenous peoples do not identify with these constructs but find themselves assessed and case managed based upon them. This extends colonialism and runs counter to a core principle of the profession, that being social justice. Canada is presently calling social work to participate in a reconciliation effort, although that assumes that there was a mutually beneficial relationship to restore. Some argue against that but there is a strong consensus that social work should carry its share of the burden in colonialism and self-reflect while also reaching out to build a different type of relationship with Indigenous peoples. This article reports on three projects that consider Indigenous knowledge and application to social work. Child protection is seen as a major focal point of change, as Indigenous children are significantly over-represented in the children in care population. Looking at this area of practice will help to illustrate the long roots of the colonial practices but also how current practice remains problematic.
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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 | 0.056 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.072 | 0.104 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 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".