Indigenous or Blended Model for South Asian Social Work?
Bibliographic record
Abstract
An argument about indigenous social work education often surfaces in South Asian schools of social work. In this study, central concerns around the indigenous argument, together with a review of the pervasive influence of the western model of social work in Asia is undertaken. The three author- research team, utilised a methodology that featured a desk review and an analysis of communications with select South Asian scholars and academics that were respondents for the study. Additionally, the authors present their personal reflections, that prudently address their positionality and reflexivity. The primary finding in this research paper is that the legacy of Western-influenced social work education is thriving within the region, despite criticism from different quarters about its effectiveness. The secondary finding is the admittance by respondent academics in Bangladesh, Sri Lanka, India and Nepal that relevant to their societal context, limited adaptations have been introduced and are working in their respective countries. The current research provided an opportunity to research participants to view and summarily reject claims by certain bogies that western influence in social work is solely responsible for lack of cultural appropriation within the curriculum in South Asia. The study suggests that there is a lot that can take place by way of adaptation without sacrificing the cultural elements while rearranging the social work curriculum within the region. The authors strongly advocate a blended approach as a suitable course of moderation in the re-construction attempts of social work futures in South Asia.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".