Imagineering Urban Places: Enhancing the urban design process with a futures-driven approach to design for spatial equity
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".