New farmers and food policies in Canada
Bibliographic record
Abstract
As the demographics of farmers are shifting, the ways agricultural and food policies affect and influence the decision-making and behaviours of new farmers is also changing. At the same time, there is growing interest in contesting and rebuilding Canadian food systems to address environmental and social injustices. Many new farmers are interested in agro-ecological approaches to agriculture, including both ecological practices and community-based economies. This paper examined the findings of a national survey of 1,326 new farmers, to explore challenges and opportunities in the Canadian food and farming system, as well as the municipal, provincial, and federal policies that they recommended. We also examined which programs are serving new farmers best, and how these successes could be translated elsewhere. We found that an increasing number of new farmers are coming from non-farming backgrounds and are women, potentially challenging the status quo. In particular, respondents reported facing difficulties in accessing agricultural knowledge, and that available institutional resources may not be appropriate to new types of ecological farming practices. The most significant barriers concerned affordable land and financing their developing farms. Nevertheless, these new farmers are finding diverse ways to develop their livelihoods, potentially transforming Canadian agriculture. A national food policy that works with local and regional partners and that recognizes the changing realities of new farmers is a necessary first step in helping build a sustainable, healthy, just, and resilient food system in Canada.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".