Assessing physical and digital participatory model making in urban design
Bibliographic record
Abstract
Participatory model making, a tool that allows participants to explore and discuss urban design through the 3D interactive medium of building and modifying an urban design model, is currently underutilized as a participatory urban design tool, despite evidence in the literature that supports its potential.This study assessed participatory model making by holding model making sessions with neighbourhood associations across Vancouver, British Columbia.The participants used either a simple physical (LEGO®), or digital (SketchUp®) model making tool, that was set up to allow for the easy creation of models to explore the topic of sustainable neighbourhood design.This study found that participant's ability to engage with the subject matter and discuss amongst each other was substantial when utilizing the model making tools.Furthermore the participants expressed that they strongly preferred using the model making tool to more traditional tools like maps and markers.Although both digital and physical model making tools were effective, the two mediums created a different model making process and group dynamic that is important for consideration when choosing between a digital or physical model making tools.Digital model making encouraged a more focused and controlled scenario while physical model making encouraged more wide open and energetic scenario.I would like to acknowledge and thank Professor Renee Sieber for her guidance and mentorship (and patience) over the past several years and two academic degrees from McGill University.Her knowledge, expertise, passion and critical eye have consistently guided me throughout my academic career.I would also like to thank my committee members, Professor Maged Senbel (UBC) and Professor Nik Luka (McGill) for their contributions and feedback.Thank you to all the neighbourhood associations across Vancouver who took time to meet with me and help me organize every local model making sessions.Thank you as well to my field assistant Brittany Jang for her assistance in logistics and data collection.I must also
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".