The How of Doing Decoloniality in Social Work: Indigenous and Antioppressive Yarning
Bibliographic record
Abstract
This article draws on yarning (reciprocal Indigenous storytelling) with 15 Aboriginal and Torres Strait Islander social service providers to explore the question of how we can support change in decoloniality and antioppressive practice in social work. The methodology foregrounds the voices and world views of Indigenous people in order to disrupt Western thought and to dialogue with and extend antioppressive social work theory, knowledge, and practice. The yarning (qualitative) data were collected in five agencies providing Indigenous social services in close conjunction with Indigenous communities, and produced three strong themes: neo-liberalism and funding challenges; working from a new model; and nurturing and sustaining long-term relationships. The findings present recommendations for decoloniality including decentring Western foundational concepts and practices; providing space for cultural practice(s); Indigenous yarning or the cocreation of new narratives; centring Indigenous ways of knowing, being and doing (cultural safety and cultural humility); collective and individual critical reflexivity; visioning for all; and connection to Country. The article ends with further reflections on Indigenous resilience and healing, reclaiming sovereignty, and nurturing resistance.IMPLICATIONSNeo-liberal funding policies slow and disrupt decolonising efforts in social service organisations.Decolonisation of social service practices and policies involves patient, long-term relationship building and close attention to ongoing flexible and respectful collaboration with Indigenous Peoples and communities at all levels.Critical reflexivity among frontline workers and across social service agencies can help maintain decolonising efforts and support new options for social work practice and policy.
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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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.031 | 0.124 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| 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".