Bibliographic record
Abstract
A guiding principle of modern traffic safety professionals attempting to reduce the risks associated with traffic is to holistically address traffic safety as a multidisciplinary partnership issue. The systems approach focuses on the relationships and dependencies between the various elements of the traffic system. The C3-R3 Systems Approach to traffic safety is introduced; the building blocks of the C3-R3 approach are three entities (the road user, the vehicle, and the road environment), three pre-crash timeline phases (creation, cultivation, and conduct), and three postcrash timeline phases (response, recovery, and reflection). This approach is proposed as a framework for multidisciplinary traffic safety professionals to research traffic safety issues in an integrated, systematic manner. The C3-R3 approach provides an enhanced systematic framework that more clearly identifies the stages at which traffic safety professionals can intervene to promote road safety. The graphical representation of the C3-R3 system, as presented, emphasizes the convergence of the entities as the timeline proceeds toward a crash event and their subsequent redivergence in the postcrash timeline. Every combination of entity and timeline phase represents a cell in the C3-R3 system; the contents of each cell represent the individual elements that traffic safety professionals need to focus on and understand in order to reduce the crash risk. The C3-R3 Systems Approach represents a starting point to encapsulate the systems approach concepts in traffic safety. It is expected that as more professionals adopt systems thinking, the C3-R3 approach will continue to evolve, expand, and improve.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".