How do runners select their shoes? An in-store experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We aimed to identify factors that influence running shoe selection and how salespeople and runners experience the in-store selection process. In a cross-sectional design, we surveyed 101 runners (buyers and non-buyers) and 38 salespeople in specialty running stores. Surveys contained questions about demographics, factors influencing shoe choice, sources of footwear advice/education, conscious behaviour, and perceived influence of salespeople on selection. There were no significant differences between buyers and non-buyers regarding how much runners thought about their purchases (i.e., level of consciousness). Salespeople were significantly younger than runners and believed a greater number of factors and sources of advice influenced shoe selection. Runners most frequently identified fit, comfort, and gait analysis or injury prevention as most influential in selecting shoes, in that order. Salespeople believed comfort was the most important for runners. Buyers and non-buyers prioritised advice on running shoes from salespeople, friends, and family, while salespeople primarily got their information from peers. Buyers and non-buyers visiting speciality running stores largely reflect the same population. Salespeople advising runners significantly differed from their target clientele in several regards and overestimated their influence on runners’ selection. We caution runners to carefully consider the advice from salespeople as many employees make recommendations that are not evidence-based and may have limited experience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 it