Geographical offender profiling: Dragnet's applicability on a Brazilian sample
Bibliographic record
Abstract
Abstract Even though geographical offender profiling is already widely used and researched in countries such as England, Canada, and United States, it is still severely overlooked in Brazil, as there are no researches on the subject using a Brazilian sample. Therefore, the present paper aims to start filling this gap by analysing the applicability of the geographical offender profiling and Dragnet on a sample of Brazilian serial killers. In order to achieve this objective, the authors collected data through police records on 15 serial killers that were active between June 2009 and June 2015, in the city of Campina Grande, Paraíba, Brazil. As a result, the circle hypothesis was confirmed, considering that 66.7% of the sample fell into the marauder category. Interestingly, the Dragnet's result accurately informed the area that contained the offender's base in the same cases in which the offender acted according to the marauder model. Also, other important correlations were found such as the influence of age, intelligence, resourcefulness, and method of approach on the distance travelled to commit a murder. Those results show that the geographical offender profiling can be effectively applied in Brazil and thus is a valid investigative tool to aid police officers in serial crimes investigations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".