Voice from the field: Decolonizing subject for more just epistemology
Bibliographic record
Abstract
This autobiographical reflection explores the author’s transnational journey of decolonizing social work education, shaped by teaching experiences in Canada and Indonesia. The paper begins with the author’s transformative encounter teaching Anti-Oppressive Social Work Practice at McGill University, where engagement with diverse student identities and critical pedagogy catalyzed a deeper interrogation of positionality, power, and privilege. Upon returning to Indonesia, the author applies decolonial insights to reshape curriculum across three undergraduate courses—Introduction to Social Welfare, Social Work Theories, and Multicultural Social Work—within an Islamic university context. Drawing on literature and classroom practices, the paper critiques the dominance of Eurocentric epistemologies and calls for the integration of Indigenous, Islamic, and local cultural knowledges. Through student-centered learning, critical reflection, field-based assignments, and engagement with concepts like gotong royong and Ubuntu, the author demonstrates how decolonial pedagogy can localize theory, disrupt colonial legacies, and foster culturally grounded social work practices. This work contributes to global dialogues on justice-centered epistemologies in social work education by highlighting the challenges and opportunities of advancing curricular decolonization in postcolonial, religiously rooted contexts.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.073 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".