Assessing the phenomenon of out‐of‐school children in Nigeria: Issues, gaps and recommendations
Bibliographic record
Abstract
Abstract Since the 1950s, the Nigerian government has undertaken various reforms and assessments to improve educational planning and delivery. Schemes and legal frameworks, such as Alternative Schools, Universal Basic Education, the Open School Program and the Child Right Act, exemplify efforts to universalise elementary education in the country. Yet, up to 20 million children and youth in Nigeria are currently out of school. Utilising a critical literature review approach, validated by empirical data and correlation analysis, this paper assesses Millennium Development Goal 2 and Sustainable Development Goal 4 to identify the factors that make education inaccessible for children in Nigeria vis‐à‐vis the effectiveness of policies and government interventions. The critical literature review evaluates prior research and data to understand the systemic issues within Nigeria's educational framework. Through this approach, we identify patterns and relationships that underpin the educational crisis, highlighting both successes and persistent gaps. To quantify the relationships between variables, we employed correlation analysis. Our assessment shows a significant positive correlation between unemployment and the rate of out‐of‐school children in the country, indicating that favourable socio‐economic conditions translate to improved access to education. Additionally, geopolitical disparities, insecurity and public spending on education collectively influence educational outcomes in Nigeria. We recommend a reimagining of the out‐of‐school children phenomenon by government agencies, non‐governmental organisations and civil society to focus on household‐level intervention policies that reflect local socio‐economic conditions. This study proposes the initiation of a sovereign annual fiscal audit and expenditure tracker to monitor the flow of funds from patrons or the government to the proposed beneficiaries. Furthermore, we suggest a review of the Universal Basic Education Act of 2004 and recommend that schools are brought even closer to children in crisis‐stricken areas. The findings underscore the pressing need of targeted, context‐specific strategies to address the multifaceted barriers to education in Nigeria. Context and implications Rationale for this study : Comprehensive critical reviews on the issue of out‐of‐school children (OOSC) in Nigeria are scarce. With an alarming 20 million children not attending primary and secondary schools, a thorough scholarly evaluation of this pressing issue is urgently needed. Why the new findings matter : By highlighting key issues such as government commitment, household economy and insecurity as factors influencing OOSC rates, this study offers actionable recommendations to both the private and public sectors on how to effectively reduce the number of out‐of‐school children in line with the sustainable development goals (particularly, SDG4). Implications for policy makers, researchers and the general public : The findings of this study will assist policy makers (government bodies, schools and interested development partners) in developing strategies to tackle the issue of OOSC in Nigeria. It will help improve the design of policies and techniques to address the barriers to education access. Furthermore, it provides the foundation for future research and sensitises the general public to join hands in the advocacy for educational equity.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".