The Relevance of Science, Technology and Mathematics Education (STME) in Developing Skills for Self Reliance: The Nigerian Experience
Bibliographic record
Abstract
Science, Technology and Mathematics Education (STME) has been proved to be an indispensable factor in the economic development of any country; and for Nigeria, it has a more critical role to play. This paper examines the relevance of science, technology and mathematics education (STME) for national development and self-reliance of Nigerian citizens. A historical overview of the interconnectivity of science, technology and mathematics education (STME) and self reliance is highlighted. The current situation and challenges facing STME in Nigeria are also highlighted to bring out the theoretical relevance of (STME) for self reliance which actually has not been practically realized. The author's recommendations include the call for training and re-training of STME teachers to update their knowledge, and the provision of long vocation/weekend programmes for out of school individuals, among others.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".