Integrating Simple Environmental Impact-Based Metrics into the Undergraduate Curriculum
Bibliographic record
Abstract
The most important learning objective in green chemistry education is the ability to identify the synthesis, process, or chemical that is least environmentally harmful. Existing metrics fall short for different reasons. Mass-based metrics fail to assess environmental harm, while life cycle assessment (LCA) is much too complex to insert into the existing curriculum without displacing a significant amount of content. However, individual environmental impact-based metrics derived from LCA can be easily incorporated into the curriculum with very little instruction time and no significant displacement of content. For first year or introductory chemistry, we show how typical first-year calculation questions can be expanded to allow students to use an impact-based metric to identify the least harmful of the presented options. For upper-year courses, we propose an activity that scaffolds the LCA process of compiling data, calculating individual impact-based metrics, combining metrics, and using context to make a decision. This activity was implemented using a problem-based learning model to support multivariate reasoning through peer discussions.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".