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Record W4390096912 · doi:10.1080/23748834.2023.2286727

Ontological foundations of urban health policy ideas: the case of planning Sydney’s Western Parkland City

2023· article· en· W4390096912 on OpenAlexaff
Jinhee Kim, Evelyne de Leeuw, Ben Harris‐Roxas, Peter Sainsbury

Bibliographic record

VenueCities & Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversité de MontréalResearch Canada
Fundersnot available
KeywordsAgency (philosophy)Urban planningScale (ratio)Regional scienceSociologyPlace identitySustainable developmentKey (lock)Environmental planningPolitical scienceGeographySocial scienceCivil engineeringEngineeringComputer scienceCartography

Abstract

fetched live from OpenAlex

This case study examines the ontological backgrounds of urban health policy ideas in planning the Western Parkland City, a large-scale regional development project in Sydney, Australia. Using an empirical approach, the study identifies seven key urban health policy ideas and analyses the nature of these ideas using urban health ontological frameworks. The dominant ontological paradigms appear as the medical-industrial and urban health science paradigms with strong alignment with the sustainable urban development and healthy urban planning research traditions. Additionally, the dominant ideas adopt a view of systems that is complicated more than complex, favour change driven by structure rather than agency, and involve perspectives that transcend across multiple scales. These findings highlight the importance of recognising the influence of paradigms in shaping policies and the need for transdisciplinary approach to policymaking.

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)0.008
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0210.037
Scholarly communication0.0070.005
Open science0.0020.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.121
GPT teacher head0.454
Teacher spread0.333 · 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 designQualitative
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

Citations2
Published2023
Admission routes1
Has abstractyes

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