Bibliographic record
Abstract
Robbery is the theft of property with the threat or use of violence. Burrell (2022) argues that robbery represents the tipping point between theft and violence, as the offence involves both elements. Legal definitions vary by country, but robbery is usually classified as either a property offence (e.g., Canada, India) or a violent crime (e.g., UK, USA) (Ashmore-Hills & Burrell, 2020) and can be targeted against businesses (commercial/business robbery) or individuals (personal robbery). This differs from burglary, whereby the target is a building (e.g., bank, house). For example, stealing from a bank when it is closed and no-one is on the premises is a burglary, whereas walking into a bank when it is open, threatening staff and customers (with or without a weapon) and stealing money would be robbery. The core theme in robbery is, therefore, that the robber steals from a person/people using violence or the threat of violence to intimidate victims to achieve this (Brakovic et al., 2022; McLean et al., 2020). When researching robbery, it is important to note that recording practices vary. Many countries record the most serious offence that takes place during an incident – for example, rape including robbery elements would be recorded as rape in the UK, whereas other countries (such as South Africa) would record both rape and robbery offences for such an incident. In New Zealand, the offence recorded is based on the primary intent of the perpetrator (Ashmore-Hills & Burrell, 2020). This makes like-for-like comparisons of prevalence difficult, but the nature of robbery still means there are considerable overlaps regardless of jurisdiction. Furthermore, the harm that robbery causes (both financial and non-financial) makes this offence a priority for many police forces and communities.<br/>This chapter will outline key information about the robbery offence. It will then provide a summary of prevalence statistics for robbery (extent) before exploring the causes and consequences of the offence. The final section outlines a range of methods and tactics for tackling robbery (solutions).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".