Violent crime in Lithuania: trends and patterns in 2015–2020
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
Abstract. The paper presents the results of analysis of spatial distribution of violent crime in Lithuania. Two periods are compared: 2015–2019 that can be characterized as a period with relatively stable crime dynamics and 2020, the year of Covid-19 pandemic. Violent crime (events that have elements of direct threat to a person) was chosen because it is the type of crime that causes the most harm and because the worrying trend of its growth has been observed against a backdrop of declining overall crime. We demonstrate how the distribution of violent crime had changed in Lithuania in 2020 compared to the trends of 2015–2019 and, specifically, during the two lockdown periods of 2020 – between March 3 and June 17 and from 4 November to the end of the year.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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