Change in The Canadian Midwest: An Analysis of Land in Edmonton
Bibliographic record
Abstract
In this paper, I analyse the usage and layout of land in Edmonton, its impacts on the city’s growth and future, and propose potential solutions to the city’s current problems. Zoning is one specific area that will cause the council many political issues in the coming years. In city planning, “zoning” refers to how cities divide land into areas called “zones.” These zones typically have different regulations regarding how developers can use land and what buildings they can build. Recently, outdated zoning laws have hindered Edmonton’s climate goals and development in urban areas, which I will show in this paper. Even after zoning reform, the public remains concerned about the influence of big-money developers. However, Edmontonians are also uneasy about a more significant tax burden due to economic uncertainty. I propose regular 20-year zoning bylaw revisions, creating an inventory of unutilised and underutilised public land, and taking steps towards common land ownership. These solutions combine Edmonton’s future with current sustainability and development goals in mind. I also propose the creation of a land value tax and will show why it would be the most viable source of revenue for the city. This would reshape land ownership in Edmonton and Canada while boosting potential government resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".