Step By Step: Suburban Active Transportation Planning in Spring Hill, Tennessee
Bibliographic record
Abstract
Suburban form produces car dependency with its circuitous routes, segregated land uses, and sprawling development. Active Transportation (AT), defined as non-motorized travel modes such as walking and cycling, has the potential to provide suburban residents with alternative mobility options. In 2015, Spring Hill, Tennessee, a city with suburban form and no dense urban core, adopted a Bicycle and Greenway Plan (BGP) to develop an AT network. This thesis seeks to understand how AT network plans are institutionalized, maintained, and expanded through policy and other implementation tools in order to accelerate progress on the expansion of AT infrastructure in Spring Hill. The thesis begins with four case studies: Spring Hill, Tennessee; Jefferson County, Alabama; Apex, North Carolina; and Mississippi Mills, Ontario, Canada. The case studies revealed that infrastructure, policy-making, and social programs must go hand in hand for a successful network. The thesis continues with sixteen one-on-one interviews of municipal staff, elected officials, and local developers in Spring Hill. The interviews addressed perspectives on walkability, experiences with AT implementation, and ideas for improving citywide pedestrian accessibility. The interviews reinforced that separated land uses and sprawling development limit the potential for walkability. Additionally, they revealed that greenfield development has been responsible for the majority of the BGP build-out thus far. BGP implementation would benefit from more buy-in from the city through dedicated funding streams and better use of existing programs that target pedestrian infrastructure. This work contributes to Active Transportation research by investigating the unique challenges of establishing walkability in rapidly growing suburban places.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".