Infusing Sustainability Science Literacy through Chemistry Education: Climate Science as a Rich Context for Learning Chemistry
Bibliographic record
Abstract
Global science is paying increasingly urgent attention to sustainability challenges, as evidenced by initiatives such as the working group determining whether Earth has moved from the Holocene to the Anthropocene Epoch on the geologic time scale and the interdisciplinary efforts to define and quantify our planetary boundaries. Despite the fact that much of the scientific work underlying these initiatives is based on measurements of fundamental chemistry parameters, sustainability literacy has not been incorporated in any systematic way into the undergraduate chemistry curriculum. We report here on the philosophy and implementation of a NSF-funded initiative, Visualizing the Chemistry of Climate Change (VC3), which provides an exemplar for developing strategies to fill that gap, focusing on climate change, one of the defining sustainability challenges of the 21st century. VC3 targets the strategic first year university and college chemistry courses that are common to the program requirements of many science and engineering majors. The overall goals of the VC3 project are to infuse climate literacy principles into the learning of representative core topics in North American general chemistry courses for science majors, while demonstrating that learning core chemistry topics by starting with an important rich context is a viable approach.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".