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Record W4407570576 · doi:10.31542/ea8tc405

Change in The Canadian Midwest: An Analysis of Land in Edmonton

2025· article· en· W4407570576 on OpenAlexvenueaboutno aff

Bibliographic record

VenueMacEwan University Student eJournal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsnot available
Fundersnot available
KeywordsZoningRevenueLand useGovernment (linguistics)Environmental planningSustainabilityLand-use planningUrban planningLand developmentBusinessLocal governmentNatural resource economicsGeographyEconomicsPublic administrationPolitical scienceCivil engineeringEngineeringLawFinance

Abstract

fetched live from OpenAlex

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 Recently, outdated zoning laws have hindered Edmonton’s climate goals and development in urban areas, which I will show in this paper.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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.323
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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