Climate Action can “Flip the Switch”: Resourcing Climate Empowerment in Chemistry Education
Bibliographic record
Abstract
Traditional approaches to chemistry curriculum for undergraduate students prioritize coverage of fragmented individual topics rather than employing systems thinking to embed chemistry concepts in immersive holistic contexts vital to our planet’s future, such as climate change. Many students are eager to understand and tackle climate change, drawing on political, socio-economic, sustainability and chemistry perspectives. However, educators face substantial barriers in resourcing climate empowerment through chemistry education. This paper outlines interactive resources and activities educators can use to help students engage with climate literacy and action, grounded in an emerging understanding of key concepts in chemistry. These resources draw from the work of 14 third- and fourth-year undergraduate students at The King’s University who were learning about climate change in an environmental chemistry class. The students collaborated in small groups and as an entire class to develop learning activities, pilot activities created by others, articulate topics for educators, and perform several rounds of peer review. Topics chosen for this publication include systems thinking and Earth systems connections; the nature of and evidence for climate change; Earth’s radiation balance, greenhouse gases, and climate engineering; models to forecast the future; and chemistry’s role in solutions. Together, the students developed activities and learning outcomes they hope others will use to connect climate change to cognitive, affective, and kinesthetic learning in chemistry.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".