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Using Urban Planners to Increase City Sustainability through the Development Process

2018· preprint· en· W2783095324 on OpenAlexaboutno aff
Jacob Adejare Babarinde

Bibliographic record

VenuePreprints.org · 2018
Typepreprint
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityPlannerUrban planningBusinessEnvironmental planningProcess (computing)Public serviceService (business)Political scienceMarketingPublic relationsCivil engineeringEngineeringGeographyComputer science

Abstract

fetched live from OpenAlex

This paper presents an experimental scenario aimed at bridging the gap between the cities we have and the cities we need, not only in the 21st century but also beyond, using the integrated tools of development control and holistic land development model to achieve a planner-led vision of city sustainability. Due to scathing criticisms against the development control system, the paper contends that planners as development approving officers and public interest specialists are better positioned than allied professionals to increase city sustainability through a holistic development process that benefits from the concept of strong sustainability posited by ecological economists. The paper adopts a seven-stage, 56-cell land development matrix (model) to simulate the development of the typical high-rise residential condominium in Ontario, supported with secondary data and the author’s ground experience as a planner and realtor with condominium customer service experience across Toronto and Mississauga cities between 2008 and 2017. Findings reveal that planners can seize the opportunity of being leaders of the development team to synergize the risks and value creation in land development that are key drivers of strong sustainability. The paper suggests some policy implications for averting disasters like fire hazards and terror attacks in high-rise residential buildings.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.113
GPT teacher head0.369
Teacher spread0.256 · 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 designObservational
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
Published2018
Admission routes1
Has abstractyes

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