An Interactive Planetary Boundaries Systems Thinking Learning Tool to Integrate Sustainability into the Chemistry Curriculum
Bibliographic record
Abstract
Sustainability has a molecular basis that suggests a central role for chemistry in addressing today’s challenges to Earth and societal systems, and this role requires educators to see chemical reactions and processes as integral parts of dynamic and interconnected systems. Despite this prospect, few accessible resources are available for students and educators to facilitate systems thinking in chemistry for sustainability. We have developed an interactive digital learning tool ( https://planetaryboundaries.kcvs.ca ) based on the Planetary Boundaries framework, which uses interactive visualizations to help users better understand Earth system sustainability challenges and helps chemists and educators connect substances, reactions, and chemistry concepts to sustainability science. The tool highlights the fundamental role that chemistry plays in regulating the individual biophysical Earth system processes and in determining their control variables. It incorporates key features of a systems thinking framework by illustrating the dynamic interconnections among the processes and their control variables and demonstrates change of the Earth system over time. Finally, the interactive tool provides educators with accessible entry points to support the integration of chemistry curriculum content with sustainability considerations.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.091 | 0.014 |
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".