Determinants of Crime Rate: The Case from Regions of Mindanao, Philippines
Bibliographic record
Abstract
This study examines the factors influencing crime rates across various regions in Mindanao, Philippines, from 2009 to 2022, addressing its economic and security challenges. Like many developing countries in Asia, the Philippines faces disparities in economic growth, with Mindanao lagging behind other areas in terms of development. This issue is further intensified by travel advisories from foreign governments, such as the United States, United Kingdom, Canada, and Australia, impacting tourism and foreign investment. By analyzing GRDP per capita, mean years of education, unemployment rate, urban population, police operating expenses, and police visibility, the research uses panel regression analysis to determine significant crime predictors. Results show that mean years of education have a considerable positive relationship with crime rates, suggesting that higher education levels may be linked to increased crime, particularly through the involvement of educated individuals in sophisticated crimes, such as white-collar crime or drug-related activities. Meanwhile, police visibility has a significant negative relationship with crime rates, indicating that a higher police presence is an effective deterrent, as criminals are reluctant to engage in criminal activities when a strong police presence is evident. Other variables, such as GRDP per capita, unemployment rate, urban population, and police operating expenses, were insignificant. These findings underscore the complexity of crime factors and the necessity for strategic police allocation and education reforms, providing insights for policymakers in addressing crime. The study’s implications extend beyond the Philippines, offering insights for other countries facing similar challenges in balancing economic growth and crime prevention.
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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 | 0.004 | 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 | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".