A review of methodologies employed in roadside surveys of drinking and driving
Bibliographic record
Abstract
The Department for Transport is to commission a new roadside survey of drinking and driving in 2008. In preparation for that research, this report reviews the roadside surveys previously conducted in the United Kingdom (UK) and then considers similar studies conducted in other parts of the world, with the primary objective of identifying examples of best practice which could inform the design of a new UK roadside survey of drinking and driving. Following a review of the UK research conducted to date, and a discussion of the various possible research objectives that a roadside survey might try to address, the report reviews specific studies that offer potential improvements to the methods used previously in the UK. This section of the report is structured around the fundamental aspects of the research methodology that could be changed, namely: 1. Who collects the data?; 2. When are the data collected?; 3. Where are the data collected and how are the sites selected?; 4. What data are collected? To answer these questions, recent examples of best practice from Belgium, the Netherlands, Canada and the USA are discussed in detail.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".