Crime Reach Analysis Within the Environmental Backcloth
Bibliographic record
Abstract
The criminal propensity to commit crime is heavily influenced by surrounding environmental factors. Determining the various aspects and features within the environment that contribute to increased criminality and the scope of their impact can further the conceptualization of the environmental backcloth. To this end, a graph model of the environmental backcloth becomes a useful tool for analyzing the various elements that form the environment. By using a graph model, analytical tools from the mathematical field of Graph Theory can be applied to the environmental backcloth. In addition, various algorithms used in Social Network Analysis can be adapted and applied to this graph model. In particular, this paper explores how the concept of reach — used in Social Network Analysis to identify central actors — can be adapted and applied to a graph model of the road network of the City of Vancouver, Canada. This analysis focuses specifically on the relationship between the presence of stores and the rate of crime near a junction. A strong correlation was found between reach to stores and reach to crime demonstrating that areas with a high store presence tend to have high crime rates.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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; both teacher heads agree on what is shown here.
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".