“Land imaginaries” in Western Canada: (financial) neoliberalism, agrarianism, and the contemporary politics of agricultural land
Bibliographic record
Abstract
This article examines contemporary political controversies over agricultural land in the prairie region of Canada. We suggest that contemporary land politics reflect elements of continuity and change in a distinctive “land imaginary” connected to the region’s history and recent restructuring. While neoliberalism, and more recently, financialization, have been the main drivers of restructuring in recent decades, certain strands of agrarianism continue to shape social relations in the agricultural sector. We present three case studies, the first of which examines the controversy over institutional investment in farmland, focusing on the Canada Pension Plan’s large-scale purchase of Saskatchewan land. The second case study examines conflicts over the deregulation of government-run community pastures, with implications for the ranching sector, environmental conservation, and the future of native prairie. Our third case study focuses on the proposed sale and land-use conversion of government-owned pasture land in Alberta, dubbed “Potatogate”. We examine the role of farmers, ranchers, governments, NGOs, and private interests in shaping debates over land ownership and use. We argue that these conflicts reveal a tension between (financial) neoliberalism and agrarian arguments and values, with significant differences across agricultural sub-sectors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".