Crash risk for low-income and minority populations: An examination of at-risk population segments and underlying risk factors [R31]
Bibliographic record
Abstract
There has been little substantive examination of the specific nature of the crash risk experienced by specific age and gender cohorts among the lower-income populations or how the daily activities of each of these cohorts may affect crash risk. In general, lower-income and minority populations are treated as monolithic groups, with little effort to identify specific population cohorts at disproportionate risk. This study examines pedestrian and cyclist crashes occurring in lower-income areas in Broward, Palm Beach, and Miami-Dade counties. This study is designed to address three specific objectives: (1) estimate the relative risk of pedestrian and cyclist crashes in lower-income communities, compared to their more affluent counterparts, to understand the nature of the pedestrian and cyclist crash risk in lower-income areas; (2) identify specific at-risk population cohorts within lower-income census block groups, stratified by age, gender, and the time of day to develop a profile of the unique characteristics of crashes experienced by pedestrians and cyclists in these areas; and (3) examine the effect of the commuting patterns on vehicle-pedestrian and vehicle-cyclist collisions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".