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Record W2582637532 · doi:10.55016/ojs/sppp.v9i1.42613

Municipal Revenue Generation and Sprawl: Implications for the Calgary and Edmonton Metropolitan Regions Derived from an Extension of “Causes of Sprawl” (Technical Paper)

2016· article· en· W2582637532 on OpenAlexaffabout
Melville McMillan

Bibliographic record

VenueThe School of Public Policy Publications · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsUrban sprawlMetropolitan areaExtension (predicate logic)RevenueGeographyRegional scienceEconomic geographyEnvironmental planningTransport engineeringBusinessLand useComputer scienceEngineeringFinanceCivil engineeringArchaeology

Abstract

fetched live from OpenAlex

There are good reasons to expect that attributes of local public finance may impact urban land use and, specifically, sprawl. A detailed and novel investigation of U.S. metropolitan areas published in 20061 provides substantial insights into the causes of sprawl, but it overlooks the main characteristics of local public finance (taxes and user charges). Using a subset of the data matched to city public finance data, a parallel analysis gives insight into the impacts of local public finance on sprawl. There is evidence that greater reliance on local property taxes reduces sprawl. The evidence that user charges (primarily for water, sewerage and solid waste services) could have a similar effect is weak but suggestive. The combined effects of a high reliance on property taxes and user charges (compared to typical levels) might reduce sprawl by as much as one-third. For Calgary and Edmonton, this means that the current heavy reliance on property taxes in both cities reduces sprawl and that the adoption of alternative local taxes—that reduce reliance on property taxes—is expected to increase sprawl. Further analysis of the impacts of local public finance on urban sprawl is warranted.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.079
GPT teacher head0.361
Teacher spread0.282 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2016
Admission routes2
Has abstractyes

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