Managing public data for whose benefit?: a case study analysis of accessing land titles in the Canadian prairies
Bibliographic record
Abstract
In Canada, land registries fall within provincial governments’ jurisdiction and therefore, approaches to management vary across the country. A trend of privatization has occurred in some provinces, marking changes in how governments approach the management of their land registries. Research on land administration and management of public services has not included thorough examination of the levels of accessibility of land registry data to the public. While there are some members of the general public who can efficiently access data, others face challenges when seeking to acquire data for various public interest purposes. This multiple case study analysis centres on the Canadian prairies where provincial governments’ decisions regarding land registries are developing within a context of modernizing public services. Through semi-structured interviews with 21 individuals and document analysis of various resources including Hansard records, I seek to explore the political economy of land registry management and the inclusion of the private sector in modernization and service delivery. I highlight the streamlining of services according to lawyer-centric systems and products, which have developed based on an orientation towards creating, improving and marketing services towards professionals in legal, real estate and financial sectors. I connect this trend to the evolving commercialization and marketization of land registries occurring in cases where private sector service providers (in partnership with provincial governments) are leveraging data to generate capital for shareholders and owners. In doing so, the “public” nature of land registry data is challenged and compromised. Based on these developments, I usefully discuss the implications of lawyer-centric systems on the accessibility of data to members of the public who do not fit within the mainstream category of clientele, but maintain a right and hold valid interests in accessing public land registry data.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.031 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".