Data-Driven Analysis: A Comprehensive Study of CPS Case Outcomes in 42 English Counties (2014-2018) with R Analytics
Bibliographic record
Abstract
Abstract This scholarly work thoroughly examines a dataset of criminal activities, specifically emphasizing the process of data pre-processing, cleansing, and subsequent analytical procedures. The dataset utilized in this study is obtained from the Crown Prosecution Service Case Outcomes by Principal Offense Category (POC), covering the period from 2014 to 2018 and including forty-two counties in England. The initial stage of data pre-processing encompasses a systematic sequence of procedures, which includes deleting superfluous percentage columns, arranging the data in chronological order, aligning the columns appropriately, removing special characters, and converting the data types as necessary. Appropriate measures are taken to address missing data to protect the integrity of the dataset. The descriptive analytics section examines multiple variables, encompassing county, year, month, area, and crime categories such as homicide, sexual offenses, burglary, etc. Clustering techniques, such as K-means and Hierarchical clustering, are utilized to identify underlying patterns within the dataset. Classification models such as Support Vector Machines (SVM) and Random Forest are utilized to forecast case outcomes. This is facilitated by employing thorough reporting techniques and doing Receiver Operating Characteristic (ROC) analysis. Time series analysis, namely using ARIMA modeling, is employed to comprehend the temporal patterns present in crime data. The paper presents a comprehensive analysis of the performance of ARIMA models, offering hypotheses, model descriptions, accuracy matrices, and visualizations as evaluation tools.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".