Bibliographic record
Abstract
The speed at which people elect to travel is affected by vehicle and road design; by limits to speed and enforcement of those limits; by traffic control, signs, and markings; and so forth. The speed at which people travel, in turn, affects road safety. In this context two questions arise: ( a) How is the evolution of speed over time and space affected by what drivers do? ( b) How does speed affect safety? This paper reviews what is known, notes the gaps in knowledge, and describes where opinions differ and why. Unfortunately, despite decades of speed measurement and monitoring, the evolution of speed over time is poorly documented, and the understanding of what drives the evolution is largely missing. It is known that speeds evolve over time, but not why; it is known that there is some spillover of the change from one road to another, but its size or extent cannot be predicted. This is a neglected field of inquiry. More is known in answer to Question b. There can be no reasonable doubt that if speed increases while other conditions (vehicles, roads, medical services) remain unchanged, the accidents that occur will tend to be more severe. However, the prevalent and strongly held belief that the greater the speed, the higher is the probability that accidents will occur is, at present, not well supported by research. Even so, given a change in mean speed, one can predict the consequences in injuries and fatalities and this paper discusses how to do so.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".