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.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".