The Effect of the Covid-19 Pandemic on the Crime of Theft
Bibliographic record
Abstract
This research aims to assist the police institution in preventing theft crimes that increased during the Covid-19 period by mapping areas prone to theft crimes based on the incident's location and the level of intensity of theft crimes. This research is empirical, managed quantitatively by collecting data through documentation and literature study. The results showed an increase in the number of theft crimes by 42.65% in Makassar City during the six months of the coronavirus pandemic period. This research also succeeded in mapping the locations prone to theft crimes, mostly in residents' homes rather than in the business center, the central area of money circulation. The research results also show that almost all sub-districts in Makassar City are the places where theft crimes occur, dominated by medium and high categories symbolized by red, yellow, and green color. This study recommends that police institutions pay more attention to residential areas, which are the areas where theft crimes most occur during the pandemic period. Furthermore, this research implies that it can become a reference for the police institution to prepare efforts to prevent theft crimes in Makassar City and other areas during the Covid-19 period.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".