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Reassessing the Case for Development Charges in Canadian Municipalities

2022· article· en· W4311625638 on OpenAlexaffvenueabout
Andrew Sancton

Bibliographic record

VenueCanadian Planning and Policy / Aménagement et politique au Canada · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsWestern University
Fundersnot available
KeywordsSloganSubdivisionMetropolitan areaGrowth managementUrban planningEconomic growthBusinessPolitical sciencePublic administrationEconomicsGeographyLand useLawEngineeringPolitics

Abstract

fetched live from OpenAlex

“Growth should pay for growth.” This slogan — the common justification for development charges — is rarely challenged in municipal circles. Development charges evolved from post-1945 subdivision agreements and were initially accepted by most developers as a mechanism for enhancing the likelihood that current residents in a municipality would agree to new development. They now add as much as $135,000 to the cost of a new house in some parts of the Greater Toronto Area. If we wish to lower the cost of housing in our prosperous cities, we must consider reverting to the past practice of having municipalities pay for new infrastructure associated with development. Such a policy — still largely in place in metropolitan Montreal — would lead to increased levels of municipal borrowing and modest increases in property taxes. This report explores the origins of development charges in the United States and Canada, and examines how they have been assessed in the academic literature.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.339
Threshold uncertainty score0.767

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.037
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0550.032
Scholarly communication0.0200.006
Open science0.0040.009
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0060.000

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 head0.039
GPT teacher head0.330
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2022
Admission routes3
Has abstractyes

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