CRIME DATA ANALYSIS
Bibliographic record
Abstract
This dataset holds crime rates of many U.S. cities and states along with precise information on violent and property crimes and demographic data including population counts. The dataset has a datestamped record for every entry so that one can analyze across different time frames, with the time attribute starting from the year 2022 and continuing through at least 2025. The central emphasis lies with the frequency of certain categories of crimes—violent crimes (murder, rape, robbery, aggravated assault) and property crimes (burglary, larceny, motor vehicle theft)—and the respective population figures, allowing for per capita crime rate calculation. The dataset consists of 305 rows and 16 columns, with all rows containing usable data, although some values may require formatting or standardization. From the data, we incurred two key points: first, that crime entries span across a meaningful recent timeframe (2022–2025), enabling temporal trend analysis; and second, that the dataset is relatively clean in structure but still benefits from light preprocessing. This dataset is highly beneficial to various stakeholders, such as local law enforcement agencies, government offices, criminologists, and the press, all of whom can leverage the information to monitor crime trends, manage resources optimally, and frame educated policies for public safety. The dataset also has the potential for researching the socio-geographic dynamics of crime, urban development strategies, and aiding community action or awareness programs.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.031 |
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".