Bibliographic record
Abstract
Dominant social work approaches are increasingly problematized. In many contexts mainstreamed social work practices and knowledges are inappropriate and eclipse alternative ways of knowing, being, and doing. Moreover, dominant approaches, promoted through professional imperialism, may be harmful, perpetuating colonial perspectives, ignoring structural conditions, underlining social control, and advancing decontextualized individualism. In order to become relevant to local populations, social work must build on traditional helping strategies and use contextualized worldviews/knowledge(s) to shift focus to parochial concerns. The authors explore alternative social work paradigms, paying attention to Indigenized, Indigenous, culturally authentic, local, developmental and decolonized models. The authors then conceptualize contextualized social work. This synthesized approach allows for the centering of Indigenous/local knowledge(s), an engagement with the impact of colonization and oppression, and responsiveness to local conditions. The authors consider specifically contextualized social work education, noting emergent literature regarding practice exists, but less so in the area of education. The distinguishing features of such education are highlighted, and policy supports identified. It is recommended that contextualized social work education be promoted to ensure future social work practitioners are able to work in a meaningful, relevant and respectful manner in all 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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.034 | 0.042 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".