Bibliographic record
Abstract
Geographic profiling (GP) is an investigative technique that involves predicting a serial offender’s home location (or some other anchor point) based on where he or she committed a crime. Although the use of GP in police investigations appears to be on the rise, little is known about the procedure and how it is used. To examine these issues, a survey was distributed internationally to police professionals who have contributed GP advice to police investigations. The survey consisted of questions designed to assess: (a) how geographic profiles are constructed, (b) the perceived usefulness and accuracy of GP, (c) whether core GP conditions are examined before profiles are constructed, and (d) the types of cases in which GP is used. The results suggest that geographic profiles are commonly used in operational settings for a wide range of crime types. This appears to be true even when GP conditions are violated. In addition, general perceptions of GP accuracy and usefulness appear to be high, but this is particularly true for respondents who use computerized GP systems (compared with spatial distribution strategies, such as centroids, or educated guesses). Computerized GP systems are also the most commonly used GP approach among our respondents, especially for those who have received formal training in GP. Although preliminary in nature, the results from this study help enhance understanding of how GP is used in police investigations around the world, and under what conditions. The survey also provides directions for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.015 |
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".