Creation of a rough runnability index using an affordance-based framework
Bibliographic record
Abstract
Running is one of the most popular forms of physical activity. To date, no literature explores association between the features of the built environment and running. A limited amount of literature uses walkability as a proxy for runnability, thereby misjudging the differing goals of walking and running: leisure, sport or commute. We create a rough runnability index using an affordance-based framework as a conceptual and methodological attempt to quantify features of the built environment that facilitate or hinder running as a form of leisure or sport activity. Three indices are created in the City of Surrey, British Columbia, Canada using pixelated edges. We find that areas in the downtown core and near high traffic routes show low safety and general runnability, whereas areas near parks and in low traffic, residential areas show higher safety for runners. Representing runnability using pixelated edges allows for sub-block level analysis of runnability as experienced by runners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".