Portals for planners: current state of open land development data in Canada
Bibliographic record
Abstract
As municipalities struggle to keep up with the rising amount of development applications and population growth, open data has been overlooked as a solution to improve the development review process. This paper explores the current role of open data portals in providing land development data in five large urban centres in Canada, assessing the issue of availability. A set of 10 land development datasets, from an urban planning perspective, was investigated, as well as a case-study analysis on the respective municipalities open data history and initiatives. It was found that land development data is more prevalent in larger populated cities; simple geographic location points are common; and there is inconsistency within each municipality on how information is released publicly. Through these findings, recommendations were made for various municipal staff members to address the challenges in opening essential land development data. Key words: open data; open data portal; open government; urban development
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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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.041 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".