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Record W4242785894 · doi:10.32920/ryerson.14647860

Barriers to meeting density targets in suburban communities - a case study look at Markham Centre

2021· preprint· en· W4242785894 on OpenAlexaffabout
Scott A. Jackson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsZoningContext (archaeology)Urban planningEnvironmental planningBusinessCommunity developmentPublic administrationEconomic growthGeographyRegional sciencePolitical scienceCivil engineeringEngineeringEconomics

Abstract

fetched live from OpenAlex

Ontario provincial policy has identified 25 Urban Growth Centres in the Greater Golden Horseshoe. Most of these centres are located in municipalities where suburban policies and practices are well entrenched in community development. Markham Centre is studied in detail, where interviews were conducted with municipal planning staff and development industry professionals, to investigate how municipalities are facilitating urban development while trying to meet provincial density targets. The study further attempts to understand the challenges which confront willing developers in building higher densities within the suburban planning context. The role of outside agencies, development charges, parkland dedication and parking requirements, were all identified as barriers to high density development, while the co-operative relationship between the municipality and the developers, the structure of the planning department, the use of an advisory committee and the use of a more prescriptive zoning bylaw were all heralded as aiding development within the city.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.525

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0170.004
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.289
Teacher spread0.261 · 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 designQualitative
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
Published2021
Admission routes2
Has abstractyes

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