Diffusing Education for Sustainability into Teacher Education Programme in Nigeria: A Theory in Use
Bibliographic record
Abstract
The benefits of integrated understanding of the issues, acquisition of the knowledge and the skills, understanding of the right perspectives, and development of appropriate values in respect of the intertwined actions and reactions of environment, economy and society make Education for Sustainability(EfS) an innovation that must be explored – more importantly for communication within the formal education sector, because such learning may be more sustainable than the one received through the informal system. This paper therefore explores an application of the “Diffusion of Innovation Theory” which identifies information, its communication, the social system and time as the four essential elements involved in the dissemination of information about an emerging problem; and in this context, an education that combines the study of development and environment as the innovation that should be communicated within the formal education sector, using the socially critical orientations and based on negotiations between the teacher and the recipients(learners). The paper thus reflects on the educational implications of the theory and suggests that curriculum of teacher education institutions must be reviewed to accommodate the learning content of EfS (e.g. climate change, green economy, democracy equity and social justice, structural change, reclamation of social bonds, waste disposal/management/recycling) and theorising more on learner -friendly approaches, so as to have a trickle down effects on the younger generation of school children who are the final recipients of environment and development-related education.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".