Bibliographic record
Abstract
Social media and web-based tools are making it easier than ever to increase public participation in the planning process. This article summarizes six cases in which technology was used to make charrettes more accessible for community stakeholders. In Denver, the use of software and a keypad made it easy to prioritize comments and conduct anonymous polls concerning plan alternatives for Arapahoe Square. El Paso, Texas, used a virtual town hall web site as part of its comprehensive planning process. When Alberta, Canada, was considering transforming an automobile-oriented thoroughfare to a Main Street-style corridor, they used newspapers, a website, Twitter and Facebook to keep the public informed before and during the charrette. Ashland, Oregon replaced its unmoderated e-mail list with a monitored online public comment system in order to solicit public opinion on a controversial topic. Community members in Somerville, Massachusetts were encouraged to take and post photographs that documented perceived strengths and weaknesses in the built environment. The U.S. Department of Transportation’s Volpe Center has implemented a project to foster technological innovations that assess the impacts of various land use and transportation alternatives on climate change and rise in sea-level. The system was tested in a pilot program in Cape Cod, Massachusetts. These examples highlight how technology can supplement, but not replace in-person charrettes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.072 | 0.010 |
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".