Check the score: Field validation of Street Smart Walk Score in Alberta, Canada
Bibliographic record
Abstract
Walk Score® is a proprietary walkability metric that ranks locations by proximity to destinations, with emerging health promotion applications for increasing walking as physical activity. Currently, field validations of Walk Score® have only occurred in metropolitan regions of the United States; moreover, many studies employ an earlier Walk Score® version utilizing straight line distance. To address this gap, we conducted a field validation of the newest, network-based metric for three municipal types along a rural-urban continuum in Alberta, Canada. In 2015, using street-level systematic observations collected in Bonnyville, Medicine Hat, and North Central Edmonton in 2008 (part of the Community Health and the Built Environment (CHBE) project), we reverse engineered 2181 scores with the network Walk Score® algorithm. We computed means, 95% confidence intervals, and t-tests (α = 0.05) for both sets of scores. Applying the Clifford-Richardson adjustment for spatial autocorrelation, we calculated Spearman's Rank Correlation Coefficients (rho, r s ) and adjusted p-values to measure the strength of association between the derived scores and original network scores provided by Walk Score®. Spearman's rho for scores were very high for Bonnyville (r s = 0.950, adjusted p < 0.001), and high for Medicine Hat (r s = 0.790, adjusted p < 0.001) and North Central Edmonton (r s = 0.763, adjusted p < 0.001). High to very high correlations between derived scores and Walk Scores® field validated this metric across small, medium, and large population centres in Alberta, Canada. However, we suggest caution in interpreting Walk Score® for planning and evaluating health promotion interventions, since the strength of association between destinations and walking may vary across different municipal types.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".