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Record W7135159827

Imagineering Urban Places: Enhancing the urban design process with a futures-driven approach to design for spatial equity

2023· article· en· W7135159827 on OpenAlexaff
Tatiana Efremenko, Gosia Grzesikowska, Honorata Grzesikowska

Bibliographic record

VenueOCAD University Open Research Repository (OCAD University) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsUrban designUrban planningPlace identityNormativeProcess (computing)Spatial designEquity (law)Design processCapability approachDesign knowledge
DOInot available

Abstract

fetched live from OpenAlex

Imagineering of cities, or the process of translation of creative and imaginative ideas into a real input to be designed in urban places, is a task to be tackled. Yet another question, whose imagination it is, and how exactly it is produced? Modern urban design has been highly professionalised, as well as largely following the logic of normative and predictive path of city visioning. Following the normative approach to urban design implies using available data for planning, which is based on big sets of data and knowledge available. Contemporary challenges of urban transition require us both to rethink the ways we think about urban places and inhabitants, as well as adopt new methods of engagement in city visioning. Design and futures studies have the potential to aid the design of urban places to become more exploratory and imaginative. At the same time, there is a necessity to find how to “engineer” and put into practice the imaginary and visioning output. On the other hand, there is a need to ensure that cities are designed to accommodate the needs of diverse groups of living beings – human or non-human. Design discipline can provide an opportunity to face both challenges and give a direction of a futures-driven and more-than-human perspective to the design of urban places. The objective of the article is to explore how a design futures-driven approach can inform urban design methodologies to create more inclusive urban futures. It describes the development of the methodology and its implementation in five pilot studies on different spatial scales. The process of the participatory methodology consists of creating, immersing, and developing ideas in various scenarios and mapping them on the masterplans with the help of three sets of cards – (i) What-If questions cards, (ii) Agent cards, (iii) Design cards. Finally, it discusses the main results and benefits that a futures-driven design approach can bring to urban design.

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)0.023
metaresearch head score (Gemma)0.020
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 score0.023
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0070.023
Scholarly communication0.0130.015
Open science0.0030.014
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0160.002

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 head0.126
GPT teacher head0.345
Teacher spread0.219 · 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
Published2023
Admission routes1
Has abstractyes

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