Reflections from first-generation small-scale vegetable farmers
Bibliographic record
Abstract
Renewal of the agriculture sector requires an influx of young farmers, either members of farming families or first-generation farmers. The latter face distinct challenges (Bloomfield, 2023; Magnan et al., 2023). This study seeks to understand some of their motivations and challenges in order to inform policy changes to support and encourage more first-generation farmers. Agriculture has long been regarded in Canada as not only economically but also culturally significant. Yet less than 1% of the population are recognised as farmers by the latest census data (Statistics Canada, 2021). In the last three decades alone, Canada has net lost nearly 150,000 farmers and the average age of a Canadian farmer is now 56. Only 8.5% of Canadian farmers were under 35 in the last Agricultural Census, compared to 20% in 1991, and that percentage has been declining steadily since 1931 (Clapp, 2023; Magnan et al., 2022; Qualman et al., 2018; Statistics Canada, 2006, 2022). In particular, the number of young people from farming families staying in agriculture is declining. Several reports, including that of the Royal Bank of Canada Climate Action Institute, show that a majority of farmers do not have a succession plan in place although, within the next decade, 40% will retire (Yaghi, 2023). People from non-farming backgrounds find it difficult to enter the profession due to barriers that include prohibitive costs and lack of training. To ensure that Canada can feed its growing population, we must address the farmer shortage by understanding the experiences of new—particularly young—farmers.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".