Prince George's County Vision Zero Story Map
Bibliographic record
Abstract
In the spring 2020 semester, the University of Maryland I-Consultancy tasked a team of college-level information scientists to consult with Prince George’s County’s Department of Public Works & Transportation (DPWT) on the development of a Vision Zero story map. This project was sponsored by the University of Maryland’s Partnership for Action Learning in Sustainability (PALS) and overseen by DPWT employees, Andrea Lasker and Nima Upadhyay. The Department of Public Works & Transportation oversees approximately 2,000 miles of roadways in the County and is responsible for ensuring safe road conditions by removing snow and ice, installing and upgrading streetlights, and much more. During the summer of 2019, Prince George’s County announced it would join the Vision Zero Initiative, a worldwide project aimed at eliminating traffic fatalities and severe injuries. The County’s participation requires that DPWT produce a story map or website that showcases data trends in crashes in the County. The I-Consultancy team was tasked to help the department produce this deliverable. This report gives an overview of the Vision Zero story map and provides information on accessing, maintaining, and updating it.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.093 | 0.026 |
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".