Application of Clustering Methods on Sexual Harassment Cases
Bibliographic record
Abstract
Sexual harassment is one of the most common crimes in Indonesia, this act of sexual harassment can occur in daily life regardless of time, whether at work, on the street, or at home. Sexual abuse can come from unknown people, people who have hate, even people we care about. To solve problems that often occur in some cases including cases of sexual harassment that often occur in women based on certain factors, resulting in trauma to the victims who suffer physically, sexually, and psychologically, are required quick action to reduce the number in cases of sexual abuse in the area that often occur using clustering methods so that later it is expected to help the agency in socializing so that the community is more alert while in the place. From the testing conducted using 20 sexual harassment cases data there are 3 groups, namely group 1 there are 9 data and 2 groups there are 5 data and group 3 there are 6 data and it can be known that in cluster 1 is a group in the case data on sexual harassment based on the factors that are many causes with a total of 9 data and located in the age group (X) is 12-16 years, and for the group Sexual Harassment (Y) namely Physical Harassment and causing factor (Z) that is a lot due to individual factors.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".