Beyond the Deficit Model: Organic Chemistry Educators’ Beliefs and Practices about Teaching Green and Sustainable Chemistry
Bibliographic record
Abstract
The rise of global environmental issues has stressed the importance of sustainability and green chemistry teachings. Nevertheless, these topics remain largely untouched in most post-secondary organic chemistry lecture courses. This article investigates the barriers to integrating green and sustainable chemistry into organic chemistry classrooms and was guided by questions like: Do organic chemistry educators have knowledge of green or sustainable chemistry, do they think it is relevant to their field or courses, and do they have resources to make change? A series of one-on-one, semi-structured interviews were conducted with 8 active faculty members in the field of organic chemistry at Canadian universities. Qualitative data analysis was carried out on interview transcripts using an inductive thematic approach and the application of the Framework Method. Major themes of content, structure, resources, management, and individuals were identified at the intersection of green chemistry, organic chemistry, and general barriers to educational reform. These findings will ultimately be used to inform curriculum development and supplement the creation of open-educational sources for organic 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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".