The Impact of Poverty on Senior Secondary School Girls' Prospect for Tertiary Education in Nigeria
Bibliographic record
Abstract
Poverty has degraded lives for centuries; and human deprivation is still persistent in the developing countries of the world. It is in this regard that this study examined the impact of poverty on senior secondary school girls’ prospect for tertiary education in Nigeria. The aim was to ascertain the extent to which the prospect of senior secondary school girls for tertiary education is susceptible to poverty. The study was conducted adopting empirical design. The data used for the study were time series data. A stochastic model was specified for the study to show the impact of poverty on senior secondary school girls’ prospect for tertiary education in Nigeria during the period under study (1992 – 2011). The ordinary least square (OLS) regression technique with econometric views 3 software was used to analyze the study’s data. The estimated result showed that both poverty and unemployment are significant determinants of senior secondary school girls’ prospect for tertiary education in Nigeria. It is therefore suggested among other things that Governments should not only direct policy actions towards encouraging the education of the girls from poor homes by creating separate scholarship platforms for them that can fund their education from secondary school to university level; but also should extend the free education policy to secondary school level in order to give every child from a poor home the opportunity to have at least secondary education. This would help to reduce the girl-child trafficking for sex work, as well as all poverty stimulated juvenile delinquencies in the country.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".