Community-Designed Participation: Lessons for Equitable Engagement in Transportation Planning
Bibliographic record
Abstract
Despite years of legally mandated public engagement for transportation planning, there is often little evidence that this results in more equitable processes or outcomes. Recently, there has been interest in improving engagement by having community-based or advocacy groups design, lead, and implement public engagement activities. This research examines two separate engagement processes—one led by a public agency, and one designed and carried out by community advocates—to understand the opportunities and barriers for community-led engagement in transportation planning. We assess how these processes differed in: (1) representation of equity-deserving groups in respondents, (2) conceptualization of equity and community needs, and (3) transportation priorities identified in the surveys. While neither process fully reflected city demographics, the community-led process was more representative of equity-deserving groups. We found key differences in priorities between the community- and agency-led surveys, and by respondent identity. Areas that were identified as a high priority in the agency-led survey, such as traffic congestion, were lowly ranked in the community-led survey, as respondents prioritized safety and lower fares. Critically, community- and agency-led processes used substantially different framings of transportation equity, along with different understandings of community needs and experiences, which could have a significant impact on the development of future transportation plans. Community-led strategies require significant resources and capacity to undertake, but meaningful participation in the design and implementation of engagement processes has the potential to better engage a diversity of perspectives and reflect community priorities.
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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 | 0.103 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".