Contextual Ranking by Passive Safety of Generational Classes of Light Vehicles
Bibliographic record
Abstract
Summary Each year, the Bulletin d’Analyse des Accidents Corporels (BAAC) data set gathers descriptions of traffic accidents on French public roads involving one or several light vehicles and injuring at least one of the passengers. Each light vehicle can be associated with its ‘generational class’ (GC), a raw description of the vehicle including its date of design, date of entry into service and size class. In two given contexts of accident, two light vehicles with two different GCs do not necessarily offer the same level of safety to their passengers. The objective of this study is to assess to what extent more recent generations of light vehicles are safer than older vehicles on the basis of the BAAC data set. We rely on ‘scoring’: we look for a score function that associates any context of accident and any GC with a real number in such a way that, the smaller is this number, the safer is the GC in the given context. A better score function is learned from the BAAC data set by cross-validation, under the form of an optimal convex combination of score functions produced by a library of ranking algorithms by scoring. An oracle inequality illustrates the performances of the resulting meta-algorithm. We implement it, apply it and show some results.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".