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Record W4407867330 · doi:10.3828/tpr.2024.66

Coase’s ‘The Lighthouse in Economics’ revisited: a planning note on the view of building facades as a public good

2025· article· en· W4407867330 on OpenAlexaff
Lawrence W.C. Lai, Kwong Wing Chau, Terry K. W. Lam, Ken S.T. Ching, Frank T. Lorne

Bibliographic record

VenueTown Planning Review · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsNew York Institute of Technology
Fundersnot available
KeywordsCoase theoremArchitectural engineeringEconomicsPublic economicsNeoclassical economicsMicroeconomicsEngineeringTransaction cost

Abstract

fetched live from OpenAlex

This interdisciplinary article follows up on a discussion of the lighthouse as a public good in terms of planning in this journal by elucidating the neo-institutional economic proposition that views of building facades along streets are common property and are public goods in the sense that their consumption is mutually non-exclusive. Hence, planning interventions are justified from an economic point of view to ensure the adequate supply of this good. This proposition is set within the context of the arguments for and against a lighthouse being a public good with reference to Coase’s 1974 paper and recent interpretations. Some planning implications are discussed. This article was published open access under a CC BY-NC-ND licence: https://creativecommons.org/licenses/by-nc-nd/4.0/ .

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0030.016
Scholarly communication0.0060.008
Open science0.0010.002
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.061
GPT teacher head0.298
Teacher spread0.236 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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