Bibliographic record
Abstract
This chapter introduces the importance of tackling environmental unsustainability by starting with a scientific understanding of Earth as a complex system of interconnected biophysical processes. Chemistry regulates many of these processes, but the chemistry curriculum has been dominated by reductionist approaches that fail to show how concepts, processes, and data can be viewed holistically to help understand and address sustainability challenges. Educators are encouraged to write learning outcomes (LOs) for their students that go beyond isolated chemistry concepts. These LOs should equip students with a set of competencies that weave together systems thinking (addressed through cross-cutting concepts), core understandings, and fundamental practices. This approach is illustrated by introducing chemistry students to the Planetary Boundaries sustainability framework, which provides a systematic approach to understanding nine Earth system processes, how they are interconnected, and how they change over time. An interactive planetary boundaries learning tool created by the King’s Centre for Visualization in Science is presented that shows how the Earth system changes over time, how the Earth system processes are interconnected, and how to map teaching and learning of core chemistry topics to sustainability using the Planetary Boundaries framework. Classroom ready activities are provided that connect chemistry concepts to the climate change Earth system process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".