THE CHALLENGES OF TECHNICAL AND VOCATIONAL EDUCATION IN MITIGATING CLIMATE CHANGE INDUCED CATASTROPHES IN NIGERIA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article focuses on the challenges of technical and vocational education in mitigating climate change induced catastrophes in Nigeria. The concepts of climate change and related areas were discussed in the paper including the causes and effects of climate, as well as, issues of prevention, preparation and adaptation processes. The roles that technical and vocational education may play in preparing citizens to prevent, adapt and mitigate the effects of climate change are presented. These include technical assistance; conducting research with a view to improve the quality of predictions of future changes to regional and environmental conditions; and changing the attitudes of citizens through education and public enlightenment to achieve a balance between ethics and the management of the environment. In light of these issues, the authors view technical and vocational education as an effective and significant tool in ameliorating the effects of climate change. It is recommended that technical and vocational education practitioners use their understanding of science and technology to deal with challenges posed by climate change in Nigeria.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it