Building Support for Transit-Oriented Development: Do Community-Engagement Toolkits Work?
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,034 | 0,071 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,008 | 0,008 |
| Communication savante | 0,019 | 0,025 |
| Science ouverte | 0,005 | 0,022 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».