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Record W4386864606 · doi:10.1111/gean.12377

What's a School Worth to a Neighborhood? A Spatial Hedonic Analysis of Property Prices in the Context of Accommodation Reviews in Ontario

2023· article· en· W4386864606 on OpenAlexafffundabout
John Merrall, Christopher D. Higgins, Antonio Páez

Bibliographic record

VenueGeographical Analysis · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsThe Scarborough HospitalUniversity of TorontoMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCapitalizationAccommodationContext (archaeology)Closure (psychology)Property valueValue (mathematics)Capital cityPublic economicsEconomicsGeographySociologyRegional sciencePolitical scienceEconomic geographyPsychologyMathematicsLawStatisticsFinanceReal estate

Abstract

fetched live from OpenAlex

Due to a change in capital funding formula, many school boards across the Province of Ontario engaged in Accommodation Reviews to rationalize the supply of school capacity. This process led to numerous school closures and raised important policy questions regarding the economic value of a school in terms of its capitalization into property values and, by extension, how the closure of a school might affect local neighborhoods. To explore these questions, this research uses spatial hedonic methods to estimate the implicit value of accessibility to schools in the City of Hamilton, Ontario. Spatial Durbin model results provide evidence of a significant negative correlation between distance to schools and housing prices in the Canadian context. This suggests that accessibility to schools is capitalized into property values and that the closure of a neighborhood school may result in potentially significant losses of economic value in communities.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.029
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.239
Teacher spread0.203 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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