Exploring ethical issues embedded in the call for indigenisation of social work education in Nigeria
Bibliographic record
Abstract
Social work is an emerging profession and discipline in Nigeria with its pedagogical content and to an extent, the process being largely Western in design. This has led to a continuous call for indigenisation of social work education to service the peculiar needs of Nigerians. While indigenisation of social work education is a step in the right direction, efforts targeting this goal may give rise to some unexplored ethical issues. We explored some perceived ethical issues that may arise in the process of indigenisation of social work education using qualitative data collected from 15 educators from three universities in Nigeria. The data were coded with NVivo 12 and analyzed thematically. Findings revealed that in attempts to Africanise social work education, educators insisted that some issues like non-heterosexuality are unacceptable and un-African; thus, emphasized that students should not be taught/exposed to practice with sexual minorities. Also, some educators stated that the Nigerian culture of masculine superiority should be sustained. These perspectives are discriminatory, oppressive and against social work ethical principles. We recommend measures including (re)training of social work educators to reconcile their ethical and religio-cultural differences to better prepare future social work practitioners with local and ethically guided knowledge for contemporary Nigerian society.
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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 | 0.033 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.028 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".