An Examination of Alarm System Deterrence and Rational Choice Theory: The Need to Increase Risk
Bibliographic record
Abstract
A growing body of evidence is indicating that burglary prevention can be achieved through the strategic application of target hardening measures developed under rational choice theory. Many of these measures are supported by scientific research and include information suggesting that alarm systems act as a deterrent. However, the application of alarm systems as a stand-alone deterrent measure is only supported by rational choice theory if the risk of apprehension is increased. During the last decade, advances in technology have resulted in a significant change in the fundamental nature of burglary. Burglary is an increasingly profitable business, to which alarm systems may no longer pose a significant risk of apprehension. Complicating matters further, practices are being implemented by the alarm industry to reduce burden of false alarms on police services. I will, through literature review, and data analysis, examine two seemingly separate issues, the changing nature of burglary and false alarm verification. These issues will be inextricably linked and contrary to rational choice theory shown to be reducing the risk of apprehension resulting from alarm response. Additionally, it will be shown that the alarm industry's singular focus on alarms as a deterrent may be impairing the application of other effective situational security measures. The use of alarm systems as a stand-alone security strategy are being impacted negatively and more complex solutions are supported under rational choice theory. When combined as part of an overall security strategy including effective false alarm verification technology, alarm systems can play a vital role in increasing the risk of apprehension.
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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 | 0.023 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".