Analysis of the burglary phenomena : problem solving unspecified temporal break and enters in the City of Burnaby
Bibliographic record
Abstract
Crime is a complex phenomenon and environmental criminology provides a powerful conceptual framework for analyzing the criminal event.Environmental criminologists begin their study of crime by asking when and where crimes occur.Burglary is a serious crime with negative psychological, emotional, and fmancial costs to the community.Temporal data are collected by criminal justice agencies and provide fairly reliable data sets.Most temporal data collected for burglary are not based on the exact time of event occurrence; instead, the victim gives the investigator an earliest and latest potential time of occurrence.This leaves a time range or window in which the crime could have occurred.Since a large amount of police incident data on burglary is range data, discarding it would ignore the majority of incidents recorded.However, decisions on how to incorporate time ranges can yield very different perceptions of the burglary event.Recent work on this subject has produced a relatively new technique called aoristic analysis, which estimates the probability of a criminal offense occurring within a certain time span.After a discussion of aoristic analysis and other conventional approaches to the range issue, it is argued the technique is a step forward from other previous temporal analysis techniques, but has inherent limitations that prevent it from being a final solution to the time "range," "window" problem.of Daythe actual time of day on a 24-hour clock (e.g.burglary took place at lpm so it would be recorded a s 13:OO hrs) to be distinguished from the minute of the day (which would be the 780th minute) Time Windowthe period between some recorded start time and some recorded end time where the exact time is not known. Time Range -See Time WindowPatternis a term used to describe recognizable interconnectedness of objects, processes or ideas.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".