An Exploration of Intersectional Farmers in Networks of Care within Ontario Agri-food Systems
Bibliographic record
Abstract
Through feminist, queer, and food justice agrarian praxes, equity deserving farmers have mobilized networks of care to improve local food security, promote land stewardship, and bolster agri-food relations. Informed by feminist care literature and queer theory, networks of care are interdependent systems of relationships, connections, and meanings rooted in our capacity to care for and through ourselves, others, and the environment. Care is an action, responsibility, and ontology, that moves towards ideas of a ‘good life’ for us, others, and the environment. Historically, North American visions of an agrarian ‘good life’ did not represent the diverse reality of agri-food systems. This research in progress will explore the experiences of equity deserving farmers, who self-identify as queer and racially marginalized, in networks of care. Through the experiences of ten equity deserving farmers, this study aims to conceptualize networks of care in Ontario agri-food systems. This exploration will be conducted through one-on-one online interviews and drawing exercises to co-produce an understanding of how and why networks of care are conceptualized in agri-food systems. It is expected that these conceptualizations will outline how and why equity deserving farmers define and engage with networks of care, identify critical relationships and connections, and reflect on their empowerment and identity within agri-food systems. These conceptualizations can contribute to a more intersectional understanding of how networks of care can be mobilized to support current and future equity deserving farmers and their agri-food systems.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.023 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".