MétaCan
Menu
Back to cohort
Record W4246099783 · doi:10.32920/ryerson.14656785

Evaluating suburban brownfield redevelopment incentives

2021· preprint· en· W4246099783 on OpenAlexaffabout
Graham Robert Wilson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsWilfrid Laurier UniversityToronto Metropolitan UniversityLaurentian University
Fundersnot available
KeywordsBrownfieldRedevelopmentIncentiveBusinessEnvironmental planningFinanceNatural resource economicsEconomicsCivil engineeringGeographyEngineering

Abstract

fetched live from OpenAlex

Suburban contaminated property (brownfield) redevelopment projects in peripheral or lower-density municipalities often do not have the same expected returns as urban brownfields in dense cities like Toronto, which are aided by high residential sale prices. A survey (n=17) of stakeholders’ opinions found that brownfield redevelopment costs and complexity had increased since changes to environmental and planning regulatory frameworks were made. Existing financial incentives for brownfield redevelopment were reviewed in selected Greater Toronto and Hamilton Area (GTHA) municipalities. A pro forma analysis of a hypothetical mid-rise residential construction scenario was developed to test the current incentives against current market conditions (condo sale prices) in these municipalities, which were often not sufficient to make a project feasible in areas of low condo sale prices. A combination of incentives was found to be effective, and was recommended to be implemented by the Town of Whitby, which has many brownfields but no financial incentives

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)0.027
metaresearch head score (Gemma)0.063
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.063
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0010.001
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 head0.131
GPT teacher head0.424
Teacher spread0.294 · 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 routes2
Has abstractyes

Explore more

Same topicEnvironmental Justice and Health DisparitiesFrench-language works237,207