Implications of Resistance to Automated Speed Enforcement and Red-Light Camera Implementation
Bibliographic record
Abstract
Automated speed cameras (ASE) and red-light cameras are highly effective tools for reducing overall car crashes. ASE and red-light cameras help reduce most crashes, such as fatal crashes, injury crashes, and property damage crashes worldwide. Millions of people die yearly because of car crashes, and many more are severely injured. Due to the high number of fatal and injury crashes, to reduce deaths, many countries across different continents in the world started enforcing ASE and red-light cameras to mitigate the number of crashes. ASE and red-light cameras are enormously effective measures to reduce the totality of crashes. Unfortunately, despite the proven efficacy, many states in the United States and worldwide prohibit using ASE. Some prohibit red-light and speed cameras, such as New Hampshire, South Carolina, Maine, West Virginia, and Texas. Certain states only prohibit red-light cameras, including Montana and South Dakota, and some states do not allow speed cameras, counting Wisconsin and New Jersey. This paper will examine the reasons behind prohibiting automated speed enforcement technology. Additionally, the study will collect and analyze data from the United States, Canada, and Saudi Arabia regarding the amount each country levies for penalties and fines for ASE violations. Some of the public believe that automated speed enforcement techniques are a method for the government to generate revenue. Others argue that the profits are used to educate the public about traffic safety rules and use it to improve the public infrastructure. This study aims to establish whether the funds from automated speed enforcement fines are used to educate the public and improve infrastructure.
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 | 0.008 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".