Crime prevention program of an Upland municipality in the Philippines
Bibliographic record
Abstract
This study aimed to know the crime prevention programs of the Municipality of Bontoc in Mountain Province as to their indigenous crime prevention programs, the implementation of crime prevention efforts of police and public officials as well as the extent of participation of the residents of the same place. There were 356 respondents of the study who were composed of 44 Bontoc Municipal Police Station personnel, 30 public barangay/municipal/provincial officials, and 282 residents of Bontoc. This study was conducted in the first quarter of 2016, using the combination of qualitative and quantitative approaches to answering the problems of the study. After the investigation, it revealed that indigenous crime prevention practices in Bontoc include pechen system, ator system, maipaila system, and fagfaga system. All these practices are used as means of crime prevention and to some extent indigenous prosecution. The crime prevention program of the Municipality of Bontoc was much implemented relative to the three (3) elements of crime such as motive, opportunity, and instrumentality. The residents sometimes participated in the crime prevention activities, however, the police and official respondents claimed that the residents often participated. The police and public officials had similar perceptions as compared to the residents’ evaluation of the implementation of the crime prevention program.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".