Exploring educators’ and students’ perspectives on harnessing indigenous knowledge and practices in social work education modules development in Nigerian universities
Bibliographic record
Abstract
Social work education modules are contested to be dominated by Western pedagogy and perspectives. Recent discourses on indigenizing social work education in Africa focused on promoting social work education curricula that will be context-specific and largely reflect indigenous knowledge and practices. This study explored the views of social work educators and students on how indigenous knowledge and cultural practices can be utilized in developing social work education modules to achieve effective education that will be responsive to the peculiar needs and social problems of contemporary Nigeria. We interviewed eight social work educators and 12 social work students from two universities in Nigeria. Findings reveal the ongoing integration of local content into social work modules and the non-inclusion of local stakeholders in the modules’ review processes. Also revealed was a lack of Nigerian research-informed module development. The students voiced concern about the nonuse of information from their fieldwork reports and experiences in the curricula informing their learning. We recommend using responsive local content and learnings from Nigerian social work research and students’ fieldwork reports in social work curricula, engaging local stakeholders, and including students’ representatives in curriculum development/review processes for more efficient education and training.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".