Communicating complexity: interactive model explorers and immersive visualizations as tools for local planning and community engagement
Bibliographic record
Abstract
Models that capture relationships between a variety of social, economic, and environmental factors are useful tools for community planning; however, they are often complex and difficult for diverse audiences to understand. This creates challenges for participatory planning and community engagement. Conducted in the community of Squamish (British Columbia, Canada), this study develops and examines tools for communicating outcomes of a community scenario modelling exercise to diverse stakeholders. These tools are ( i) a “model explorer” and ( ii) realistic, immersive visualizations. The model explorer is an online, HTML5-based tool that can be used to learn about the model, view community scenario maps, and explore potential outcomes of the scenarios. The visualizations are virtual environments that are navigated from the first-person perspective, and they were developed using a combination of ArcGIS, Trimble SketchUp, Adobe Photoshop, and the Unity3D game engine. A local government and community stakeholder focus group and public open house event were held to solicit feedback on the scenarios and tools. Findings of the research suggest that the two types of tools can be used in a complementary fashion, and tool integration can better harness their respective strengths in a manner that comprehensively communicates the implications of different development pathways to diverse community members.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".