Bibliographic record
Abstract
In Quebec, more than half of injury crashes occur on the municipal road network. The municipalities can play a major role in contributing to reduce the road accident toll, which requires a stronger partnership among the municipalities, the Ministere des Transports and the various stakeholders. To support the municipalities further in their actions, the first report of the Table quebecoise de la securite routiere, published in July 2007, proposed three recommendations on municipal partnership: share knowledge and road safety intervention tools with municipalities; design consultation mechanisms tailored to local or regional realities; establish financial assistance for road safety actions led by municipalities. These recommendations have been concretized in many actions over the past few years. Thus, a pilot project was carried out and another is in progress to prepare a road safety diagnosis and action plan for the municipal network. These projects have allowed the development of an innovative diagnostic methodology, as well as a partnership strategy and a mode of organization adapted to the characteristics of the two territories, one located on the periphery of the Montreal agglomeration, and the other in a rural area. In addition, in September 2012, a new program, the Plan d'intervention en infrastructures routieres locales (Local Road Infrastructure Intervention Plan) was established to assist the regional county municipalities and equivalent territories in planning their interventions on the local network, by producing municipal road safety diagnoses and action plans. See also in French: Les Partenariats au Quebec : pour une meilleure securite routiere en milieu municipal. For the covering abstract of this conference see ITRD record number 201310RT334E.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 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".