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Record W7028935032

Inclusionary Zoning: Identifying Possible Legal Challenges within Canada and How Best to Pre-empt Them

2020· article· en· W7028935032 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2020
Typearticle
Languageen
FieldMedicine
TopicAutoimmune Bullous Skin Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsObligationGovernment (linguistics)State (computer science)State governmentZoningLocal government
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this research paper is to explore the legal issues that surround inclusionary zoning. More specifically, the question to be answered through this research is: How can Canadian municipalities shield themselves from legal challenges that may put inclusionary zoning policies at risk of being nullified? Through inductive research byway of qualitative content analysis of 16 American court cases, in which the legal challenges posed to inclusionary zoning ordinances are then extrapolated and applied to the Canadian context, the fundamental legal issues pertinent to Canadian municipalities become illuminated. What has been observed is that there have been a number of different arguments used, against inclusionary zoning, in American case law, however, when applied to the Canadian context, the most relevant one pertains to the purview of municipal authority. In the United States, a number of ordinances have been struck down because of the obligation placed on municipalities to have explicit authority from the state before implementing inclusionary zoning. In Canada, the relationship between a provincial government and its respective municipalities is much the same. In consequence, the most critical legal challenge that inclusionary zoning policies may face, within the Canadian context, is whether explicit authority has or has not been given by the province, to its respective municipalities, to enact such policies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.119
GPT teacher head0.307
Teacher spread0.189 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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