Extended results from the measurement and analysis of safety in a large city
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents an extended version of our measurement and analysis of data from the city of Los Angeles (Ibrahim and Shafiq, 2017). More specifically, we analysed datasets about crimes that took place in Los Angeles. This dataset was prepared by the Los Angeles Police Department (LAPD) and is also updated on a regular basis. This dataset contains approximately 1.5 million records, where each record represents a crime incident in the city. We analysed multiple features of the dataset including different activities of crimes (i.e., number of crimes) in terms of year, month, weekdays, time of the day, area, victim sex, victim age, victim descent, suspect activities and crime seriousness index. In addition to it, we also analysed the reporting period of a crime incident by calculating the average reporting days (i.e., number of days the victim took to report a crime incident) in terms of multiple factors. Our analysis uncovers the unique characteristics and insights of safety measures and crime prevention in the city. This extended version of paper contains some new results and discussions. This includes new graphs for number of crimes based on suspect activities and crime seriousness index, a new graph for crimes distribution based on crime seriousness index, and average reporting period based on crime seriousness index. We introduced a section that provides discussion on potential implications of our analytical results.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it