Bibliographic record
Abstract
This document reports on 2,300 responses to a nationwide survey of older adults who cycle. The survey, open from February through September 2020, includes questions about a rider’s cycling history, current cycling habits, and falls. It includes a visual preference survey of various cycling facilities and an online journaling option for two rides subsequent to completing the survey (results of the online journals will be available in the summer 2021). Responses reflect the impact of COVID-19 on older adults’ cycling habits, the impact of aging on ability and agility, the impact of the built environment, types of bicycles, and opportunities to cycle with others. Responses were analyzed by gender and age. Questions such as cycling frequency and falls were compared to a modified version of Geller’s four types of cyclists. Key take-aways include: Many older adults will need to adapt to their changing cycling abilities with a different bicycle, a different expectation about their cycling experience, and local programs to encourage sustained cycling. A fair number of respondents learned to cycle as an adult which suggests that local programs can also encourage older adults to learn to ride and how to select a bicycle. Lower cycling rates may result from not having a bikeable or proper-fitting bicycle, or the money to fix or purchase a bike. Questions posed for further consideration include: Can education and outreach help reduce near misses? Can planning and engineering help reduce near misses, especially in areas where more older adults cycle? How can falls due to poor infrastructure or maintenance or the actions of others be reduced?
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".