Mogush, Krizek, and Levinson Page 1 The Value of Trail Access on Home Purchases
Bibliographic record
Abstract
corresponding author 4787 words + 3 tables + 3 figures = 6,287 words We use hedonic analysis of home sales data from the Twin Cities Metropolitan Area to estimate the effects of access of different types of trails on home value. Our model includes proximity to three distinct types bicycle facilities, controlling for local fixed effects and open space characteristics. Using interaction terms detect different preferences between city and suburban homebuyers. Regression results show that off-street bicycle trails situated alongside busy streets are negatively associated with home sale prices in both the city and suburbs. Proximity to off-street bicycle trails away from trafficked streets in the city are positively associated with home sale prices, with no significant result in the suburbs. On-street bicycle lanes have no effect in the city and are a disamenity in the suburbs. The following policy issues are relevant from this research. First, type of trail matters. On-street trails and road-side trails may not be as appreciated as many city planners or policy officials think. Second, city residents have different preferences than suburban residents. Third and as suspected, larger and more pressing factors likely influencing residential location decisions. The finding also suggest that urban
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.002 |
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".