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Record W7116594709

Shaping the polycentric city region: Regional plans as multiscalar urban design instruments

2024· article· en· W7116594709 on OpenAlexaboutno aff
Adrian Carter

Bibliographic record

VenueBond University Research Portal (Bond University) · 2024
Typearticle
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaUrban designUrban planningWork (physics)Urban densityUrban landscapeScale (ratio)Quarter (Canadian coin)Urban climate
DOInot available

Abstract

fetched live from OpenAlex

Urban design is often, even usually, focused at the immediate scale of street, square and urban quarter – in other words, the places where people experience the urban design qualities of the cities in which they live, work and play. Specialist urban designers, architects, landscape architects, planners, other professionals, and communities collaborate to produce these local urban design outcomes. Meanwhile, the polycentric city region has become the dominant urban form internationally, with contiguous and non-contiguous cities, towns and suburbs merging into larger functionally interrelated urban entities.1 In parallel, metropolitan and regional plans are evolving to address the new polycentric city region. These new planning instruments can work as multiscalar urban design instruments to simultaneously shape urban form and quality at scales ranging from the very local, up through town centres of various scales and intensities, to the broader regional ecological setting. This paper draws on literature and practice to demonstrate an urban design continuum spanning the local, sub-regional and regional scales in regional planning, using selected examples of regional planning documents for major western city regions. The selected plans are examined to investigate ways in which these plans act as urban design instruments at various urban scales.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.475
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.078
GPT teacher head0.248
Teacher spread0.170 · 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.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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