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Record W4407858183 · doi:10.1002/sd.3397

Sustainable Development as A Win–Win Development: A Hong Kong Empirical Study of the Tai Kwun Heritage Project

2025· article· en· W4407858183 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSustainable Development · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsNew York Institute of Technology
Fundersnot available
KeywordsWin-win gameExternalitySustainable developmentEmpirical researchDatabase transactionReuseCultural heritageBusinessValue (mathematics)EconomyEconomicsGeographyPolitical scienceEngineeringLawArchaeologyComputer scienceMicroeconomics

Abstract

fetched live from OpenAlex

ABSTRACT This empirical study of the value‐enhancing effects of a new innovative hub for art, culture, and heritage experiences (arts hub) reusing the heritage buildings of an old prison, an ex‐police station cum detention centre called “Tai Kwun” (Big Station), on neighboring residential properties, treats sustainable development as a developmental process to achieve a “win–win” production of two originally mutually exclusive environmental goods by transforming negative externalities into positive ones, as encapsulated in Yu's model. The study demonstrates the win–win idea from a neo‐institutional economic perspective. The empirical results of a regression analysis, based on a total of 1541 sets of transaction records of residential units in the vicinity of Tai Kwun transacted between January 1993 to December 2023, showed that the hub was a win–win solution for heritage conservation and residential development.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0050.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0000.001
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 head0.030
GPT teacher head0.323
Teacher spread0.293 · 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