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Record W2084677554 · doi:10.1068/a33128

Social and Geographic Inequities in the Residential Property Tax: A Review and Case Study

2001· review· en· W2084677554 on OpenAlexaffabout
Richard Harris, Michael Lehman

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2001
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMetropolitan areaProperty taxProperty valuePoliticsProperty (philosophy)LocationPublic economicsInequalityPublic financeEconomicsGeographyDemographic economicsEconomic growthTax reformPolitical scienceFinanceLawReal estate

Abstract

fetched live from OpenAlex

Although the fact is not widely acknowledged by urban scholars, because of the way that it is administered the property tax helps to shape the social geography of metropolitan areas. Research by public finance specialists has shown that cheap housing is often overassessed, and that variations in assessment ratios (the ratio of assessed to market values) usually favour the suburbs. Sales prices and assessment data from the Hamilton, Ontario, metropolitan area for 1976, 1996, and 1999 confirm these patterns and show that they are persistent. In addition, cross-tabulations by market value and location show that geographical variations in assessment ratios are caused by the inequitable treatment of inexpensive property, not vice versa. A 1998 reassessment made the situation worse. The main difficulty in reducing tax inequities is political, not technical.

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)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.009
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.077
GPT teacher head0.268
Teacher spread0.191 · 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
GenreReview

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

Citations15
Published2001
Admission routes2
Has abstractyes

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