Land data for whom? The marketization, privatization and commercialization of land data management in Canada
Bibliographic record
Abstract
Land inequality is increasingly recognized as a critical global issue, yet its dynamics and implications remain underexplored in specific contexts. This paper examines Canada’s land registry systems, which are essential for understanding land ownership trends but are largely inaccessible for public-interest research due to marketization, privatization and commercialization. Governed provincially and territorially, these registries operate primarily under the Torrens system; a colonial framework designed to facilitate settler ownership and economic accumulation. This system separates land from its historical and ecological contexts, reinforcing settler private property regimes that prioritize market interests. Through interviews, document analysis, and reflections on the authors’ experiences, our study focuses on Ontario, Manitoba, and Saskatchewan to explore the marketization, and specifically the privatization and commercialization, of land data in Canada. It addresses three core questions: How does marketization impact access to and use of land data? Who benefits from these configurations? And how do these structures constrain understanding of land ownership trends, particularly in agriculture? The findings reveal that Canada’s land data management systems favor commercial interests and profit generation, treating data as a commodity while restricting equitable access for researchers and the public. This restriction impedes efforts to understand and address critical issues such as farmland financialization–or the increase in farmland ownership and control by financial actors. By situating these findings within the broader literature on colonialism and neoliberalism, this paper outlines how and why land data management systems have proceeded as they have. Further, the study contributes to a deeper understanding of how the current structure and function of land registry systems perpetuate land inequities, and obstruct progress toward social and economic equity, Indigenous sovereignty, and public awareness of land tenure dynamics.
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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".