Exploring Road Safety Analysis and Stakeholder Engagement for Small and Medium Sized Communities
Bibliographic record
Abstract
Rondier, Cloutier, Saunier, Soto-Rodriguez and Miranda-Moreno 2 ABSTRACT 1 2 Identifying thematic issues and Accident Prone Locations (APLs) on rural local road network is 3 challenging because of the length and scope of the network and the spatial and temporal 4 variability of crashes. The objective of this paper is to explore the complementarity between road 5 safety stakeholders ’ subjective point of view and the more objective identification of APLs 6 through an Empirical Bayes (EB) method in a rural, less-dense area of Quebec, Canada. The first 7 step of the method consists in EB analyses with a spatial database containing the accident data, 8 the road network and several environmental attributes of the road sites. The second step is to 9 recruit, interview and summarize systemic safety issues based on the perceptions of various 10 stakeholders, both spatially and thematically. An application of this comparative method in a 11 local and predominantly rural county of 23 municipalities in Quebec shed light on the usefulness 12 of combining qualitative and quantitative data in the identification of systemic issues and 13
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".