Exploring the Lived Experiences of Minority Potato Farmers: A Multi-Lens Framework for Policy and Planning Reform
Bibliographic record
Abstract
The primary focus of this paper is to understand the experiences of minority potato farmers in Ontario, through a holistic approach. For the context of this research paper, the term ‘minority’ refers to small-scale farmers (<200 acres) and/or those farmers who identify as Black, Indigenous, and People of Colour (BIPOC). Though scarce, the existing literature suggests that the small-scale, urban, and BIPOC farming movements have gained momentum in Canada within the past 5-10 years. The resulting contributions to community agri-food systems and well-being have been significant; however, these contributions and their corresponding systems are understudied.Therefore, this paper uses a holistic Systems Thinking perspective as a lens to recognize the complex relationships and connections between society, animals, and the environment based on the experiences of the farmers. To understand the aforementioned areas of interest, semi-structured interviews with members of these farming minority groups were conducted. A review of the available literature on related themes has also been included.It was found that the contributions of minority farmers are particularly relevant in tight-knit, rural areas as well as in low-income urban areas. Their challenges were nuanced, with cultural or racial minority farmers facing increased emotional health barriers. Overall, the findings and recommendations support the notion that to stabilize local food systems, we must take a systematic approach to combat food insecurity, social inequality, climate change, and to bolster potato production in Ontario. Hence, we must effectively engage minority farmers who often represent changemakers in their communities and whose motivations and practices differ largely from mainstream production.
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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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.028 | 0.042 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".