Agriculture and food education of high school students in Ontario
Bibliographic record
Abstract
This research project aims to explore agriculture and food education programs, policies and curricula for high school students in Ontario, Canada. It seeks to understand the perceptions students have of their knowledge in agriculture, and evaluates the agriculture and food knowledge students actually have to determine how agriculturally literate they are. An understanding of the agri-food sector is important for a number of reasons that are discussed throughout this paper. This study employs a mainly qualitative methods approach to primary and secondary data collection. Some of the key organizations in Ontario that exist to nonformally educate young people about agriculture and food are summarized and discussed. Online surveys distributed to students in a rural and urban school of a school board in the Greater Toronto Area provide an understanding of their knowledge and perceptions. The surveys help to determine students' common sources of information and whether they would like to further explore the subject of agriculture for further learning and their career. This project also analyzed the Ontario high school science curricula to find themes of agriculture and food throughout. Lastly, recommendations for policy, programs and further research highlight the importance of youth learning about agriculture and food to ensure they are able to make informed decisions as consumers, improve their health, and become aware of the vast opportunities in the industry.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".