Participatory Modeling With QUICKScan to Shape Sustainable Urban Development
Bibliographic record
Abstract
Current spatial planning methods are often technocratic, slow, fail to use the right kind of evidence or do not involve (all) the actors needed to create support and consensus. We present a method that facilitates the use of evidence (data) in participatory spatial planning processes, resulting in a joint understanding of the most important causalities, as a means to build capacity across actors. QUICKScan is a participatory modeling method that links stakeholder- and decision maker knowledge and preferences to available spatial and spatio-statistical data, and is designed for group use in a multi-stakeholder workshop setting. We describe four urban QUICKScan applications, that vary in objective, scale and institutional setting. The most critical in organizing a QUICKScan session is to: (i) include crucial participants in a single plenary workshop (decision maker, local data expert, and local thematic experts), (ii) create an open atmosphere in which each and everyone's opinion is treated equally, (iii) dialogue is more important than an abundance of detailed spatial data, and (iv) start with simple modeling rules and iterate often while expanding the set of rules and trying out alternatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".