Conceptual and Methodological Issues in Evaluations of Road Safety Countermeasures
Bibliographic record
Abstract
Researchers have a long history in the conduct of evaluations of road safety countermeasures. However, despite the strengths of some evaluative road safety evaluations that align with previous and current thinking on program evaluation, few published road safety evaluations have followed standard conceptualization and methodology outlined in numerous program evaluation textbooks, journal articles and Web-based handbooks. However, conceptual and methodological challenges inherent in many evaluations of road safety countermeasures can affect causal attribution. Valid determination of causal attribution is enhanced by use of relevant theory or hypotheses on the putative mechanisms or pathways of change and by the use of a process evaluation to assess the actual implementation process. This article provides a detailed description of the constructs of causal chain, program logic models and process evaluation. This article provides an example of how these standard methods of theory-driven evaluation can improve the interpretation of outcomes and enhance causal attribution of a road safety countermeasure.
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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 | 0.646 | 0.777 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".