Armed burglary: a marker for extreme instrumental violence
Bibliographic record
Abstract
Purpose Most burglaries are property offenses yet some offenders perpetrate burglary for the purpose of violent instrumental crimes. Sexual burglars are distinct from non-sexual burglars because the former seek to rape or sexually abuse victims within the homes they burgle whereas the latter seek theft and material gain. It is unclear to what degree burglars who are armed with firearms or knives represent a type of sexual burglar, or perhaps a more severe type of offender who enters homes not merely to rape a victim, but to perhaps murder them as well. The paper aims to discuss these issues. Design/methodology/approach Drawing on data from 790 felons in Florida,t-test and negative binomial regression models were used to compare armed burglars to offenders who were not convicted of armed burglary. Findings Compared to offenders not convicted of armed burglary, armed burglars were involved in significantly more instrumental crimes of violence including first-degree murder, kidnapping, armed rape, armed robbery and assault with intent to murder. Armed burglary may be a marker of extreme instrumental violent offending and warrants further study. Originality/value To the authors’ knowledge, this is among the first studies of armed burglary offenders and adds understanding to the heterogeneity of burglary offenders and their criminal careers.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".