Bibliographic record
Abstract
Problem, research strategy, and findings Identifying and implementing equitable participatory planning processes is challenging for city planners. Through a qualitative analysis of the Families and Educators for Safe Cycling Project (FESC), an active school travel (AST) project in Toronto (Canada), we identify a potential new path to increase the range of voices heard by planners and decision makers. Specifically, we present community animation and animators as an effective approach for community engagement in AST planning through analyzing 27 semistructured interviews, reviewing key project documents, and coding key themes. We showcase how community animation can play a key role in the meaningful engagement of school communities by deepening and enriching the participatory planning process. We conclude by suggesting that community animators can foster more equitable participatory planning processes by working to include historically marginalized communities within urban planning.Takeaway for practice Specific groups of people, such as school communities, continue to be excluded from participatory planning processes. By providing insights into the value of community animators, this research allows planners to understand, conceptualize, and apply more equitable participatory planning processes during infrastructure development. Though the case is based on a specific program related to AST in Toronto, the results can assist planners in other communities in enriching their local engagement processes.
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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 | 0.058 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".