Application of Clustering Methods on Sexual Harassment Cases
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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it