Making Sense of Food Safety and Food Waste: Lived Experiences in Food Catering Using Systems Thinking
Bibliographic record
Abstract
Food safety and food waste have many strands and perspectives; one is how managers and staff make decisions. There is limited research on food catering services, how managers and staff deal with food safety and waste, and their decision approaches. To obtain a better understanding, this research sought the lived experiences of managers and staff. The research aim is to explore the lived experiences of managers and staff to better understand their decision behaviour approaches in controlling food safety and food waste. The way forward is to conduct a qualitative phenomenological research focusing on twenty-five purposefully selected managers and staff in Vancouver. Using organisational theory as a lens, data were obtained through semi-structured interviews and non-participant observations. The research used inductive thematic analysis, resulting in nineteen themes. The key findings were inadequate training and planning, improper practices and customer behaviours attributed to internal and external processes and systems; managers and staff lacked appreciation for using a specific approach to support decisions. The researcher introduces systems thinking as one approach to support decision making to enhance control of food safety and waste. The implications include appreciating the interrelationship of factors influencing food safety and food waste. The research limitations were the COVID-19 pandemic, time and resources, and insufficient participant experiences. The research contribution was using systems thinking as one of the approaches to make decisions to enhance control of food safety and food waste in food catering services.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".