INFORMING DESIGN CHARRETTES: Tools for participation in neighbourhood-scale planning
Bibliographic record
Abstract
The authors have been developing computer-based decision support tools to bring education, visualization and modeling to design charrettes (design-oriented participatory community planning events) and other design-oriented public workshops. These tools have been created to engage the public together with professionals in community planning and design. One goal of the work is to close a challenging gap in knowledge and understanding between professionals and stakeholder groups charged with generating and evaluating planning alternatives. Design charrettes are well supported by qualitative, design-based participatory methods that engage the public, such as with visioning and brainstorming techniques that draw out aspirations and preferences about future growth. In order to achieve more sustainable models of urban form, charrettes must also be supported by quantitative, analysis-based methods that model and evaluate performance against indicators of sustainable development such as housing density, and access to transit and services. Central to the authors’ decision support tools is a multimedia database of measured parcel scale case studies entitled ELEMENTS OF NEIGHBOURHOODS (EoN). When linked to land use plans through a Geographic Information System and related applications, these tools help a community visualize, measure and compare competing alternatives in areas of land use, transportation, environmental quality, infrastructure and cost. With tools such as these it is possible to equitably compare alternative plans through visualization and measurement based upon the schematic design descriptions and information generated by charrette-based planning process. This paper identifies the need for informed, time-sensitive public decision-making in community planning, introduces the design charrette as one effective method, introduces the authors’ decision support tools designed to close three gaps in knowledge, methods and scale when applied to the design charrette, and reviews two applications of these tools.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".