RELATIONSHIP-BASED LENDING FOR DIVERSIFIED AGRICULTURE SYSTEMS IN CANADA: HOW TRUST AND COMMUNITY MAY TRANSFORM FINANCIALIZED AND GENDERED FOOD SYSTEMS
Bibliographic record
Abstract
Our food systems, once controlled by community and built for regions, are now global in scale and serve the interests of international financial organizations and multinational agribusiness corporations. Financialization, the term referring to the increased presence of financial actors, markets, institutions and incentives have gained importance in the organization and functioning of the economy, including agriculture & land, is an issue of increasing attention in academic and policy circles. This new trend, an increasing role of these actors in financing agriculture, means agricultural production?s primary goal has shifted from food production to cash production. Current agricultural finance habitually chooses projects based on potential for asset development rather than community benefit. Those who are most likely to opt into diversified and sustainable production such as women, people of colour, of those new to the industry struggle to fund their projects. Current unsustainability is underpinned by work dynamics and social relationships that only further perpetuates gender inequity. In this paper I argue offering trust- based loans to under-represented farmers transforms power imbalances created by financialization. The research asks: to what extent do farm women in Canada use trust-based lending to grow and sustain their farms; how does informal lending influence how women farm; and in what ways does access to informal lending shape rural communities and their food systems in Canada? I used purposive snowball sampling to identify and interview ten participants, six farmers and four lenders, using semi-structured interviews. Results show farmers using trust-based loans indicates the way they farm includes community benefits and meet personal environmental and social outcomes, including local economic development. The farmers noted a strong sense of trust between lender, borrower, and customer. Many indicated without such trust as collateral loans, they would not be in business. As researchers, community activists, and not-for-profit organizations seek resolutions to our financialized food system, trust-based loans may be of significant import.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".