Conceptual and Methodological Issues in Evaluations of Road Safety Countermeasures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 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 it