Bibliographic record
Abstract
With collision volumes increasing in some communities and the social cost of collisions being high, there was a need in Ontario for Police Services and road safety professionals to have a tool that would provide them with current information to analyze their collision data and create proactive road safety plans for targeted results driven enforcement. We met with various police administration, officers, and road safety professionals in order to determine how to best meet their needs. Throughout the development process there were many lessons learned. The analytics module designed has allowed police services to reduce collisions in their communities by identifying the cause and implementing the appropriate response either through enforcement or by providing evidence to other road safety professionals for design and policy changes. // Avec un nombre d’accidents de plus en plus important au sein de certaines communautA©s et un coA»t social des collisions routiA¨res A©levA©, les services de police et les professionnels de la sA©curitA© routiA¨re en Ontario avaient besoin de disposer d'un outil qui leur fournirait des renseignements actualisA©s pour analyser leurs donnA©es relatifs aux collisions. Cet outil devait aussi aider A crA©er des plans proactifs en sA©curitA© routiA¨re en vue d’obtenir des rA©sultats ciblA©s et qui pourraient Aatre mis en application. Nous avons rencontrA© diffA©rentes administrations de la police, les agents, et les professionnels de la sA©curitA© routiA¨re, afin de dA©terminer comment rA©pondre au mieux A leurs besoins. Tout au long du processus de dA©veloppement, de nombreuses leA§ons ont A©tA© apprises. Le module d'analyse dA©veloppA© a permis aux services de police de rA©duire les collisions dans leurs juridictions, d’en identifier la cause et A aider A la mise en oeuvre d’une rA©ponse appropriA©e, par l'application de la loi ou en fournissant les informations nA©cessaires aux professionnels de la sA©curitA© routiA¨re pour la conception et rA©aliser effectivement des changements de politiques.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".