Impervious Odds and Complicated Legacies: Young People’s Pathways into Farming in Ontario, Canada
Bibliographic record
Abstract
Abstract Ontario is the most populated province in Canada and has some of its most productive agricultural soils. However, Ontario faces problems attracting youth into agriculture. Since 1991, the province has lost about two-thirds of its 18,440 young farmers, and less than 10 per cent of Ontario’s current farmers are under the age of 35. Part of these losses can be attributed to the challenges that Ontario’s youth face in becoming a farmer. To understand these challenges, it is necessary to understand the different pathways that young people take to becoming a farmer. By understanding these pathways, it becomes possible to create more opportunities and reduce the challenges that young people must overcome. This chapter will differentiate the pathways of entry into farming for young people in Ontario, highlighting differences in how they access resources, their motivations for farming, and the type of farming that they carry out. Understanding the unique circumstances facing Ontario’s young farmers can help identify ways to encourage young people to begin a career in farming.
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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) | 0.001 | 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 | 0.001 |
| 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".