Social Inequality in Early Childhood Care and Education Provision in Nigeria: A Review of Literature
Bibliographic record
Abstract
Early childhood care and education (ECCE) programme has been identified as a strong tool to break the cycle ofpoverty and effective means to establish the basis for further learning, prevent school drop-out, increase equity ofoutcomes and overall skill levels; hence all nations of the world call for effective investment in ECCE. Nigeriaembraced the idea of ECCE and made it a national agenda by incorporating it into the national policy. The essence isto establish access and equality for children all over the country, irrespective of gender, ethnicity, socio-economictransfer. Looking at the trend or the status of the ECCE for some years, it is as if there are lapses in the provision ofearly childhood education in the country. The study therefore examined existing research in early childhoodeducation in Nigeria between 2013 and 2017 (which are pre and post Millennium Development Goals documents) tohighlight the indicators of enrolment in ECCE, sex, and personnel to determine whether there is equality orinequality in the provision and identify the areas of inequality if there be any. The findings showed that inequalitystill exists in the provision of the ECCE programme in Nigeria. The enrolment in the programme is still low ascompared to other educational levels. Employment of personnel into the programme is also very low to the numberat the primary level. There was no visible data on the educational programme as from 2017. It is recommended thatthe government should show more commitment to the educational level.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".