RESCUER: Combining Passive and Active Learning Techniques to Teach Food Sustainability
Bibliographic record
Abstract
This paper discusses the creation and implementation of an experiential learning assignment focused on the United Nation’s Sustainable Development Goal (SDG) 12, which aims to ensure sustainable consumption and production patterns. Using a grounded theory approach that combines analyzing 90 senior-level marketing students’ reflective essays alongside 63 pre- and post-assignment survey responses, we develop the “RESCUER” framework which combines active and passive learning elements. We demonstrate how active learning layered on top of passive methods can be an effective means to generate more responsible consumer behaviors within a complex food supply system. Students begin with passive learning components in the form of readings and lectures (labeled Resources), before Engaging with mindfulness in an active learning activity that involves the selection, purchase, and preparation of perishable food for a salad. The framework also includes the important effects of Social influence and its role in how Cognizance and Underlying problem salience are generated. Finally included are factors that Expedite the process of generating cognizance and problem salience such as the ready availability of relevant facilities (e.g., the existence of a garbage sorting system), which can enable more Responsible consumer behaviors.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".