Public Engagement in Public Transportation Projects
Bibliographic record
Abstract
Public participation for transit projects faces a number of unique challenges compared with many other similar public investments. For example, a smaller subset of the community uses transit on a daily basis as compared with highways; moreover, public transit is seen to be limited—both spatially and demographically—in its appeal. Combined, these factors can limit the widespread engagement of the public in the development and evaluation of transit projects. Further, given the lack of direct benefits from transit, it is often more difficult to garner public support for public transport projects. Specific considerations and techniques are demonstrated that can be undertaken by planners and policy makers to actively engage the community beyond those strongly in favor of or opposed to a transit project. Strategies employed in the Region of Waterloo, Ontario, Canada, in the context of public engagement before the introduction of light-rail transit are explored. In light of these strategies and the experiences of planners in Waterloo and in conjunction with evidence from the literature, a number of conclusions are drawn regarding an effective framework for engaging a wide spectrum of community members in transit planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".