Understanding Complete Streets: How do Professionals Define a Complete Street Project?
Bibliographic record
Abstract
The Complete Streets movement has become popular throughout North America. Although the concepts and overall objectives of a Complete Street are becoming increasingly recognized in the field of transportation planning, some ambiguity exists when defining such projects based on the existing built infrastructure (e.g., bicycle lanes, road diet). This research presents data gathered from transportation planning experts working at municipalities and regional municipalities across the Greater Golden Horseshoe Region in Ontario, Canada. Using a focus group discussion as the basis of the authors' analysis, they attempt to understand how a Complete Street can be defined at the project level as well as what factors may influence this definition. The findings show that the definition of a Complete Street can be largely dependent on contextual sensitivities including surrounding land use, roadway typologies, age and maturity of a road, and the quality of roadway infrastructure implemented with right- of-way (ROW) upgrades. The planning process is also important. Considerations relating to a ROW’s feel, function and form as well as the age, mode and mobility of the road users can be taken into account to define a Complete Street. These findings will improve the knowledge and awareness among transportation planners within (and beyond) the region regarding how a Complete Street project’s built form can change in response to various urban land use contexts, as well as planning priorities and 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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".