RESCUER: Combining Passive and Active Learning Techniques to Teach Food Sustainability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it