Spatial distribution of criminal events in Lithuania in 2015–2019
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The presented map poster represents statistics for 3.46 million events reported to the police in Lithuania in 2015–2019. For eight types of events (violent crime, theft, property crime, economic crime, infringement of public policy, drug-related crime, traffic accidents, various other events), two maps at a scale of 1:3,000,000 are presented. They demonstrate the values of location quotient and the main insights into the dynamics of crime over the five-year timeframe covered by the project. Two maps at scale 1:2,000,000 show the distribution of five types of events that are directly related to the safety of persons – totalling 1.67 million records. One of the larger scale maps depicts the relative crime rate, separately for densely and sparsely populated areas. The second map shows the relative crime risk surface. The maps enable a visual analysis of the most problematic areas in Lithuania and can enable deeper further investigation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it