Influencing Curriculum Development and Knowledge of Climate Change Issues in Universities: The Case of University Of Nigeria Nsukka
Bibliographic record
Abstract
The current institutional structures and academic programmes in most universities preclude effective education and capacity building on issues of climate change. Besides, the pedagogies and curricula are centrally defined by university governance structures which are very hierarchical and rigid, and in most cases, discourage the culture of shared thinking and collaboration required for addressing complex system-related challenges such as climate change. The study aimed at influencing curriculum development and knowledge of climate change issues at the University of Nigeria, Nsukka (UNN) and its environs. To realize this, a multi stakeholder dialogue was conducted to sensitize the stakeholders on the need to include issues of climate change in their respective Faculty curricula. Over 320 participants drawn from the academia, policymakers, private sectors and the civil society organizations participated in the process that culminated in a one-day workshop at UNN in 2009. An outcome mapping approach was used to identify the most important reasons for climate-proofing the courses in the relevant Faculties of the University through curriculum review. Results show that there is need to provide a clearer understanding of climate change issues; build capacity at individual and institutional levels for climate change adaptation; provide opportunity to attract donor funds for teaching, learning, research and community service; provide conducive environment for transdisciplinarity and shared thinking on climate change issues; and transform the future leaders of tomorrow (youths) by promoting the culture of innovation for climate change adaptation. Recommendations ranged from revising existing course contents to emphasize issues of climate change (short-term approach), introduction of entirely new course modules on climate change (medium-term approach) to introduction of new degree programmes (long term approach) in the relevant Faculties of the University. Finally, a communiqué calling for urgent inclusion of climate change issues in the curriculum of the UNN was adopted by the University Administration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".