Book Review – Social work, social welfare, and social development in Nigeria: a postcolonial perspective. Mel Gray and Solomon Ahmadasun
Bibliographic record
Abstract
The book is well-written and organized, highlighting key issues and concerns about the social, economic, and political forces that have shaped and continue to challenge social development policies and welfare provision in Nigeria. The book’s chapters each focus on specific topics, starting with chapter one, which provides background on Nigeria’s ethnic and religious divisions and shows how these social and cultural differences have affected the development of social provision. Forcing the different pre-colonial ethnic groups into nation-states made it “difficult to build patriotism and emotional loyalty to a country created by a foreign invader.” The authors are cognizant that colonized societies became what they were not—homogeneous communities with their own languages, customs, and beliefs were turned into heterogeneous nations due to coerced co-existence within new borders. This is an interesting idea of how colonization and its legacies have magnified the political and ethnic conflicts, making it difficult to develop strong social structures. The book shows the gaps in the social safety network to support vulnerable sectors of the population—those children in need of protection, those living with disabilities, those with mental health issues, the elderly, and victims of human trafficking. The authors also highlight key issues related to supporting women, discussing the challenges and opportunities for social work in serving vulnerable and rights-deserving groups.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".