Exploring African-centred social work education: the Ghanaian experience
Bibliographic record
Abstract
Through the years, there has been regular discourse among African social work scholars regarding the production of indigenous knowledge in Africa. Most of the arguments hold that social work knowledge creation and production is often based on Eurocentric approaches due to the dearth of African-centered social work literature from the continent. This paper attempts to explore the intellectual and philosophical underpinnings of African-centered social work education and practice. Using Ghana as a case study, information is provided on theoretical and conceptual thought processes about African-centered social work education in response to the shortage of insights pertaining to African culture. By means of purposive and availability sampling, we recruited three graduate students to be interviewed. The interviews were analyzed using thematic inquiry. We explored African paradigms and argued that the practice of social work education on the continent of Africa should not be based on the Eurocentric approaches only, to the detriment of traditional African ways of knowing. We believe that for social work education in Africa to thrive, we should embrace indigenous African practices and values of spirituality, collectivity, interconnectedness and reciprocity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".