The environment in which behaviours are learned: a pilot assessment of high school teaching kitchens as food safety learning environments in Ontario
Bibliographic record
Abstract
Youth represent a unique audience for consumer food safety education and incorporating such education into existing curricula could facilitate delivery. However, successful delivery may depend, in part, on the facilities in which said training occurs. Since little is known about school teaching kitchen set-ups as related to food safety education, we conducted a pilot assessment of the physical learning environment of four Ontario high school teaching kitchen classrooms. We visited each classroom three times and assessed its characteristics using a modified version of the provincial food premises inspection report. Kitchen layout varied by school, and it was the built classroom characteristic with the greatest potential impact. Several characteristics unique to school teaching kitchens were noted, including whether the classroom teaching area and food preparation area were separated. Despite the variation between classrooms, all had physical characteristics sufficient to meet the minimum requirements for food service premises in Ontario. Nevertheless, this pilot assessment highlights nuanced factors unique to high school teaching kitchen classrooms that may impact the delivery of food safety education and the development of safe food handing behaviours. Findings can support conversations between public health, food safety authorities, and school stakeholders to enhance food safety learning environments in schools.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".