Inclusive Education for A Sustainable Future and Employability of Middle Level Manpower Graduates in Nigeria
Bibliographic record
Abstract
This paper investigates the role of inclusive education in promoting a sustainable future and enhancing employability in Nigeria. Primary data were sourced to assess the main objectives of the study using self-designed questionnaire with a reliability index of 0.75 Cronbach alpha and administered to 120 undergraduate students from all the three public tertiary institutions in Oyo township. Descriptive statistics, (including simple frequency, percentages and weighted values) was used to analyse respondents' personal data and to address the research questions for the study. Pearson’ correlation analysis was utilised to test the relationship between the variable of the study. The research finds a strong positive perception of respondents on inclusive education, and in terms of the relationships between inclusive education and sustainable future and inclusive education and employability with mean scores exceeding 2.50. Specifically, the study found a strong positive correlation between inclusive education and sustainable future, with a correlation coefficient of 0.85; between inclusive education and employability, with a correlation coefficient of 0.60; and between sustainable future and employability, with a correlation coefficient of 0.55. This suggests that an increase in inclusive education tends to positively improve both students sustainable future and their employability. As a result, the study recommends that the government should formulate and implement comprehensive policies to support enhanced inclusive education in teaching and learning and also institute educational interventions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".