Cross-disciplinary Perspectives on Developing Global Datasets and Indicators for Monitoring Climate Change Education and Communication (CCE)
Bibliographic record
Abstract
Climate change education and communications (CCE) has long been recognized as a critical vehiclefor increasing human and institutional capacity to mitigate against further climate change and to adapt to its impact. However, the global community lacks robust cross-national data and global indicators to measure and monitor progress in the planning, implementation, and delivery of CCE at the national and intergovernmental levels. This paper offers a synthesis of insights from interdisciplinary literature on indicator development conducted across the areas of education, education for sustainable development (ESD), communication and biodiversity and climate change. Bringing together independent histories of developing indicators for global benchmarking and target setting supports insights for the more nascent area at their intersections, that is, CCE. On the basis of the literature review, the orientation of ideal indicators could be synthesized as a guiding idea for indicator development. Ideal indicators have real-world congruence and are built on feasible data elicitation; they facilitate solutions for climate change and leverage political and public attention. In this article’s conclusion, we identify key challenges that can compromise indicators in the context of CCE (e.g., “smallest common denominator” -solutions) as well as possible countermeasures against these challenges.
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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 | 0.304 | 0.269 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.022 | 0.030 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".