Road traffic intensity of GDP and the explanation of national peaks of yearly road fatalities and of their clustering in 1970−1974
Bibliographic record
Abstract
Our new explanation of the extraordinary clustered occurrence in OECD countries of 18 national road fatality maxima during the 5 years 1970−1974 consists in adding the variable ROAD TRAFFIC INTENSITY OF GDP to a basic equation specification already containing GDP PER CAPITA. The former acts as proxy for the ratio of total (intermediate and final) to final activity in the Economy. Tests of that additional factor, which peaks naturally during the period 1970−1974, are carried out using a partial DRAG-type road safety model formulation developed in 2002 by Marc Gaudry and Stéphane Gelgoot with a dozen core variables. This choice then allows for: (i) a multinational sample of 13 OECD country 1965−1999 series extracted from the public MAYNARD-DRAG database; (ii) a decomposition of their national safety outcomes (Injured and Killed victims) as products of the frequency of bodily injury Accidents by their severity (Morbidity and Mortality); (iii) an estimation of these five equations with flexible Box-Cox forms taking autocorrelation and heteroskedasticity of errors into account with the also public LEVEL algorithm of TRIO. Results from the addition of the new indicator turn out to be fully consistent with the proposed new hypothesis that the ROAD TRAFFIC INTENSITY OF GDP complements GDP PER CAPITA as key road risk generator. Overall, latent intermediate economic output de-industrialization of the OECD to less economically developed countries is shown to be a shared common phenomenon implying delocalization not only of road victims but of other negative externalities of intermediate economic production, such as CO2 emissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".