Distracted Biking: A Review of Current State of Knowledge
Bibliographic record
Abstract
Cyclists, much like drivers, have always been engaged in multi-tasking activities like using hand-held devices, listening to music, snacking, or reading while bicycling. While distracted drivers endanger themselves and other, distracted bikers, in general present more risk to themselves than to others. Distracted bicycling, however, has not received similar interventions to address safety related issues. This study reviewed the state-of-knowledge on policies, programs, data sources, and identified data collection opportunities and research needs. Literature review conducted in this study revealed only six (6) past studies that investigated the effect of distracted bicycling. The review also found that several agencies/organizations listed the use of portable electronic devices while cycling as unsafe behavior. Some of the agencies/organizations in the United States, Canada, Belgium, Bermuda, Germany, and New Zealand have implemented interventions to curb distracted bicycling such as education, awareness programs, and legislation. The majority of the legislation enacted ban the use of headphones or earphones in or on one or both ears and few ban hand-held phones while cycling. In addition to these, one law common to all U.S. states and District of Columbia restricts cyclists from carrying bundles, articles or objects that prevent them from keeping at least one hand on the handlebars which indirectly addresses distracted bicycling.
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 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 | 0.028 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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".