MétaCan
Menu
← Back to cohort
Record W4253502745 · doi:10.32920/ryerson.14647113.v1

Regulations in Planning Understanding the Relationship of the Growth Plan for the Greater Golden Horseshoe and Housing Affordability

2021· preprint· en· W4253502745 on OpenAlexaff
Benjamin Pister

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsMcMaster UniversityToronto Metropolitan University
FundersStrong
KeywordsPlan (archaeology)Supply and demandBusinessProcess (computing)Common groundEnvironmental resource managementOperations managementEconomicsGeographyComputer scienceMicroeconomics

Abstract

fetched live from OpenAlex

This research paper explores what impacts regional regulation, and the resulting planning process, has on ground-related housing. The Growth Plan for the Greater Golden Horseshoe (Growth Plan) is a significant regulation requiring evaluation. It has been suggested that density targets mandated by the Growth Plan have continuously decreased the supply of ground-related housing in the Greater Golden Horseshoe (GGH). The combination of regional regulations and the time of processing development applications play an important role in understanding changes in housing supply. A case study of York Region displayed this concept, such that the requirements of the Province, and the resulting changes to the planning system for the Region, has contributed to decreases in supply of ground-related housing. Prices continue to rise due to increasing demand for the housing type. This research suggests four recommendations for the Proposed Growth Plan to increase the inventory of ground-related housing: 1) reducing density targets, 2) realigning the planning process to reduce time lag, 3) observing market demand, and 4) removing a one-size-fits-all policy which currently exists within the Growth Plan.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.172

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0050.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.174
GPT teacher head0.260
Teacher spread0.086 · 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 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicHousing Market and Economics→French-language works237,207→