Building Support for Transit-Oriented Development: Do Community-Engagement Toolkits Work?
Bibliographic record
Abstract
Many metropolitan areas are struggling with how to accommodate future population growth—and are looking to transit-oriented development (TOD) as a potential solution. TODs, in which densely-built, mixedincome housing is placed near transit to create walkable neighborhoods complete with amenities and retail, could house as many as a quarter of the country’s new households in coming years.1 Yet one barrier to building a significant amount of TOD housing is the unwillingness of many local residents to support some of the components of TOD, particularly higher-density construction and mixed-income housing. Often called NIMBYs (short for Not-In-My-Backyard), opposing residents can stop such developments in their tracks.Planners must “sell” the developments as beneficial to the community and the region, and follow up on their promises by creating good plans and developments. To that end, practitioners have developed successful strategies to both counter resistance and rally community support around projects. The process requires a great deal of community education and outreach at community meetings, often aided by community engagement tools such as PowerPoint presentations, brochures, activities, and other tools created for the purpose. Despite their widespread usage of these tools, however, there is little information about their effectiveness, or lack thereof, when used in the field. There is a need for research on what about these tools works, what doesn’t, and in which situations and contexts. It is this gap that this study will attempt to fill, to help inform the work of the developers, planners, and community engagement groups that use these community engagement tools in their work.Using focus groups in our case study region of the San Francisco Bay Area, this research begins the process of showing what does and does not work in these tools, and makes suggestions for how they may be altered in light of our findings. Focus group members found much to admire and much to find fault with in the tools. In general, focus group members responded to credibility, openness and honesty; relatable and specific facts, stories and examples, especially about real people as well as real places; community benefits; and connections to their existing understandings of their lives and communities. Conversely, they were quick to pick up on any kind of manipulation or deceptiveness, unsupported ideas, or ignorance of their particular community, all of which fostered mistrust and undermined the messages of the tools.
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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 | 0.002 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".