“It is the Wild West out here”
Bibliographic record
Abstract
This research builds on the emerging body of literature investigating the implications of changing land tenure relations in the Prairie Provinces, where over 70% of Canada’s farmland is located. Through an analysis of survey data collected in 2019 from 400 grain farmers, we address the following research questions: How are farmers experiencing changing patterns of land tenure and control at the local level? What challenges and opportunities do farmers face in these changing farmland markets? And, how has the entry of new actors (farmland investors) changed relationships between landlords and tenants? Our findings suggest that those farmers who are witnessing the financialization of farmland in their regions view this phenomenon with alarm. Furthermore, we show that those who rent from corporate investors are more often subject to landlord influence over production practices and pay higher rental rates than those who rent from other landlord types. Concern about farmland concentration is widespread among Prairie farmers, with a variety of negative effects identified, including increased competition over land and the decline of local communities. We recommend that future research probe how different investor types (individual vs. corporate and/or institutional) engage in land markets, examine the gender dimensions of landlord-tenant relations, and engage in analyses that challenge the current iteration of the private property regime.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".