The Path of Least Resistance: Identifying Supporters of Public and Active Transportation Projects
Bibliographic record
Abstract
The financing and implementation of transportation projects are more likely to be successful with the support of local communities. Hence, for cities and transportation agencies to develop strategies that will improve public acceptability and reduce resistance to funding transportation projects, it is important to understand differences in the levels of local support. This study used a factor-cluster analysis to segment a university population, to understand current levels of support toward transportation investments, and seek out important allies to endorse public and active transportation projects. The results of the study reveal five clusters of individuals with varying opinions toward transportation investments and distinct motivations. Strong advocates are the greatest allies for promoting public and active transportation investments. They support financing public and active transportation projects, and are well positioned to endorse the necessity and advantages of such investments. Highway and transit funders are motivated by their dissatisfaction with the current transportation system. Cycling advocates are valuable in publicizing the benefits of expanding the bicycle network. Infrequent commuters do not travel to the university as often as the other groups, and are supportive of transportation investments in general. Despite the overall positive opinion toward investing in public and active transportation projects, there is a minority of funding opponents who are generally against financing transportation projects. The results of this study will be helpful for policy makers intending to communicate the benefits of transportation projects to various community groups.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".