Bibliographic record
Abstract
Based on data from the Netherlands, Denmark, USA and UK, Jacobsen's paper in 2003 identified the non-linearity between the number of cyclists and pedestrians and the risk of injury from being hit by a motor vehicle. In other words, the more people walked and cycled, the fewer the number and rate of traffic collisions and injuries experienced by cyclists and pedestrians—a non-linear relationship. Jacobsen, termed this relationship, ‘Safety in Numbers’ (SIN), which was shown at different levels of scale, whether at an intersection, a city or a country. More recent work has since shown SIN to occur in other countries such as Australia. The (SIN) effect quickly grabbed the attention of public health promoters who were seeking ways to promote active travel, including walking and cycling. Efforts to address growing levels of obesity and inactivity in the developed world were, and still are, underway and the concept of SIN was seen to support the clarion calls for measures to promote the number of walkers and cyclists. SIN was also seen as a support by those demonising any measure that might deter the numbers of those walking and cycling, such as the mandatory use of cycle helmets. In fact, people who campaigned for increasing helmet use were lambasted and accused of potentially increasing serious injuries among cyclists by reducing the SIN effect. It is probably the public health fraternity who have done much to increase the impact of this paper, more so than those concerned with injury prevention and/or transportation safety. Arguably, the Jacobsen's paper led to a paradigm shift among planners and engineers who could think about pedestrian and bicycle safety in a different way and not be so fearful that by encouraging increases in walking and cycling they would see an increase in traffic collisions and causalities. … Language: en
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".