Global diffusion of three road safety policies, 1964–2015
Bibliographic record
Abstract
Abstract Global convergence of public policies has been regarded as a defining feature of the late twentieth century. This study explores the generalizability of this thesis for three road safety measures: (i) road safety agencies; (ii) child restraint laws; and (iii) mandatory use of daytime running lights. This study analyzes cross-national longitudinal data using survival analysis for the years 1964–2015 in 181 countries. The first main finding is that only child restraint laws have globally converged; in contrast, the other two policies exhibit a fractured global convergence process, likely as the result of competing international and national forces. This finding may reflect the lack of necessary conditions, at the regional and national levels, required to accelerate the spread of policies globally, adding further nuance to the global convergence thesis. A second finding is that mechanisms of policy adoption, such as imitation/learning and competition, rather than coercion, explain more consistently global and regional convergence outcomes in the road safety realm. This finding reinforces the idea of specific elective affinities, when explaining why the diffusion of policies may or not result in convergence. Lastly, by recognizing fractured convergence processes, these results call for revisiting the global convergence thesis and reintegrating more consistently regional analyses into policy diffusion and convergence studies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".