Religiosity and other Socio-economic Variables affecting Education for Boys in Northern Nigeria
Bibliographic record
Abstract
Although progress has been made in examining education in northern Nigeria, literature has yet to focus on the reasons why male children are prevented from going to school in northern Nigeria. This study explores the reasons why Hausa and Fulani children are prevented from gaining formal education in northern Nigeria. The aim of this study was to explore: 1) factors that hinder attainment of formal education for children in northern Nigeria; and 2) the impacts of this discrimination on the children's families, northern Nigeria, and Nigeria in general. This group of men who are educationally discriminated against is known as the almajiri. Under the almajiri system, parents send their children, mostly boys aged 4–12, to distant locations to acquire Qur'anic education. This is a qualitative study, with data gotten through key informant interviews with 11 children and youths, and relevant academic literature was used to substantiate the data collected. It was analysed using Colaizzi's (1978) method of data analysis. The KII was conducted physically and over the phone. Emerged themes included: (1) fear of indoctrination; (2) economic benefits; (3) political benefits; (4) political benefits; (5) physical abuse; (6) sexual abuse; and (7) a high rate of illiteracy. Thus, it is concluded from the findings that children in northern Nigeria are deprived of formal education. Thus, policy advocacy and engagement with religious and traditional leaders by the government of northern states would help in addressing the problems. Policy implications and subsequent recommendations were discussed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".