Empowering Tomorrow’s Problem Solvers: Nexus Thinking and CLEWs Modelling as a Pedagogical Approach to Wicked Problems
Bibliographic record
Abstract
The increasing prevalence of wicked problems, such as climate change, requires a transformation in education that equips students with the skills, competencies and knowledge to address these complex challenges. Wicked problems are characterised by their incomplete, contradictory, and ever-changing requirements, rendering them difficult to resolve due to intricate interdependencies. Nexus thinking offers a valuable approach to these problems, as it emphasises the interconnectedness of various systems, fostering a more comprehensive understanding of the challenges at hand. In this paper, we propose the use of Climate, Land, Energy, and Water (CLEWs) modelling as an innovative pedagogical strategy tool to cultivate nexus thinking among students. Building upon the pioneering CLEWs pedagogical work of Shivakumar et al., in their ‘Introduction to CLEWs’ Open Learn course, we demonstrate how this approach can be utilised in a Higher Education (HE) setting in the form of a Masters’ module for geography students.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".