Financialization and consolidation of farmland in Manitoba: interrogating the "good farmer"
Bibliographic record
Abstract
On the Canadian prairies, farmland is more than just an investment or a resource; access and control over farmland is deeply embedded in the history, culture, and identity of Canadian farmers. Land grabbing, the large-scale purchase of farmland by domestic or foreign investors, is a phenomenon on the rise worldwide and is best understood within the framework of financialization. Despite a lack of quantitative research on the topic, some of the effects of financialization in the agri-food sector are visible in Manitoba, including rising farmland prices and increasing farmland concentration, resulting in fewer and larger farms. My research investigates the dynamics of farmland ownership in four rural municipalities with high valued farmland in Manitoba. Although reliable information about farmland investment in Manitoba is limited, 39 semi-structured interviews with farmers, rural municipal officials and staff, and others involved in the agriculture industry, provide a baseline understanding of the current dimensions of farmland sales, farmer-landlord relationships, and the social and environmental implications of increasing farmland concentration. I draw on participants’ perceptions of investors to better understand how these kinds of purchases might impact rural landscapes. Furthermore, I find that farmers themselves have adopted financial logics as they make land purchases that are less rooted in the productive value of the land and increasingly motivated by the speculative value of the land. Thus, my research reveals the ways that the ‘good farmer’ framework is at work in Manitoba and is pushing farmers to make “non-economically rational” (as cited in Burton et al., 2020, p.2) decisions that are ultimately contributing to the deterioration of rural communities and environments. The thesis concludes by discussing two pathways for the future of agriculture in Manitoba: the first is that these trends will deepen and access to land and control over food production will be further extracted from the hands of local people. The second is a more hopeful possibility that farmers, civil society, and government might co-construct a different future in agriculture by redefining what it means to be a ‘good farmer’ and prioritizing community, collaboration, and profitable/viable farm businesses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".