2022-2023 Campus Travel Survey Summary of Safety and Bike Theft Questions
Bibliographic record
Abstract
This report presents some of the key results from the two new blocks of questions included in the 2022-23 Campus Travel Survey. The first block includes questions to assess the safety of pedestrians, bicyclists, and other micro-mobility users. The second block ask respondents questions about incidents related to bike theft and vandalism on campus. To assess pedestrian safety on campus respondents were asked if they had been hit while walking on campus since the beginning of fall quarter 2022 and if so, to select the mode they were hit by. The set of questions related to fall and crash of bike and micro-mobility users was shown only to respondents who indicated that they were associated with UC Davis during the 2021-22 academic year. The report presents results of questions related to safety of bike and micro-mobility users, including questions on type of fall or crash related incidents, and reasons for their fall or crash while using a particular mode. Respondents were also asked to indicate the location of fall or crash on the campus. The block on bike theft asked respondents if they were the victims of bike theft or vandalism during the year 2021-22. The questions were designed to better understand the situations under which the bike was stolen, whether it was locked indoors or outdoors, type of object the bike was locked to, and the type of lock used.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 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".