Can we design cars to prevent road rage?
Bibliographic record
Abstract
Road rage has become a serious problem in many countries as traffic density grows and the pace of modern life accelerates. Ways to reduce road rage are needed and in this paper we suggest several ways in which cars could be designed to reduce road rage. In some cases, vehicle redesign is necessary, e.g. to reduce horn blowing or headlight flashing, or to improve communication between drivers. Experiments should be made to identify frequent road ragers to other drivers and to identify road rage incidents that are in progress, e.g. by means of global positioning systems (GPS) and other warning devices. Also certain design features now in a few cars could be made more widely available, e.g. systems to prevent or at least advise against tailgating. Most of the ideas presented here would need extensive technical testing and critical examination in the light of public policy and legal product liability before they could be implemented on a broad scale.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".