Preserving stories, preserving food:
Bibliographic record
Abstract
Worldviews, cultures, spirituality, and history not only influence how societies define “food” and “waste”, they also shape how we consume food and the relationship we have with the broader food system. While food waste has emerged as a global concern and a complex “wicked problem” that impacts stakeholders at all scales of operations, the issue is often framed as an environmental and economic problem, and less so as a social problem. As the food waste literature expands at a rapid pace, there is still a dearth of studies that focus on cultural and intergenerational approaches to food preservation and food waste reduction. This exploratory study emerged from an upper-year research-based course entitled Building Sustainable Food Systems (REM 363- now REM 357) at Simon Fraser University and offers three vignettes through intergenerational and multicultural interviews from Siksika First Nation (Canada), Pakistan and China. Students from the class explored the roles of intergenerational storytelling and informal learning by conducting key informant interviews with close relatives to document traditional food preservation techniques. This study created a transformative intergenerational and multicultural bonding opportunity, which allowed students to better understand their relationships to food, culture, and their relatives. The students also documented how the relationship to food has changed over time. Findings from the study suggest that intergenerational storytelling can help reduce food waste by increasing food literacy, improving cultural connections, and raising awareness about alternative worldviews that challenge the commoditization of food.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.034 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".