The Effect of the Covid-19 Pandemic on the Crime of Theft
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
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.
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.
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 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it