“Passion Alone Is Not Sufficient”: What Do We Know About Young Farmers in Canada?
Bibliographic record
Abstract
Abstract In 2016, Canada’s 271,935 farm operators represented less than 0.8 per cent of the Canadian population (Statistics Canada 2017a). This reflects a loss of close to 120,000 farmers over the past 25 years as Canadian livelihoods continue to shift away from agriculture (about 1.4 per cent of the population farmed in 1991). Considering that less than 10 per cent of Canadian farmers are under the age of 35, it is hard to imagine these numbers rebounding anytime in the near future (Statistics Canada 2017a). Clearly, Canadian farming faces a generational challenge (Qualman et al. 2018). However, despite these generational challenges, there has been little research that focuses specifically on young farmers in Canada and their experiences in becoming “successful” farmers. Therefore, the purpose of this chapter is to provide an overview of the information available on Canadian young farmers. This overview references existing research from scholarly literature and government statistics. This is done to offer an understanding of the context within which Canada’s young farmers are embedded. A young person’s desire to farm is partly shaped by but also shapes their experiences in becoming and being a young farmer. This overview helps inform the discussion in the next two chapters that are based upon interviews with young farmers in our two case study provinces: Ontario and Manitoba.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".