Nothing is permanent except change: A case study of crime displacement in Switzerland
Bibliographic record
Abstract
This paper presents the methodological process and the main findings of a research on crime displacement between two cantons (states) of Switzerland from 2009 to 2012. Two analytical axes have been considered: displacement of crime incidents on the one hand, and displacement of offenders for offences against the Swiss Criminal Code, on the other hand. Data were provided by police statistics of the two cantons involved and by a regional crime intelligence database, supplemented by documentary analysis, interviews and field observations. Measures of crime displacement were realized with variation in crime rates, difference-in-differences estimation and weighted displacement quotient applied to a selection of offences. Findings suggest the presence of a regional crime displacement of burglary, pickpocketing, simple theft and break-in theft in vehicle between 2011 and 2012. Displacement of offenders seems to follow this trend as well and highlights a small proportion of inter-regional offenders, but who are extremely prolific. These results and the study of displacement in general shed light on the effect of crime reduction strategies and provide many prospective pathways, such as the strengthening of inter-regional collaborations between the police forces in a federal State, and the systematic use of crime intelligence to reduce mobile criminal activities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".