Collaborative Process for Preventing Violence against Women and Children in Makassar City
Bibliographic record
Abstract
This research aims to find out and describe how the collaboration process is structured in the process of preventing violence against women in the city of Makassar. Through good collaboration between the government, NGOs, communities, and victims, it is hoped to create an environment that is safe and free from violence against women and victims. Children in the city of Makassar. The research method used is a qualitative approach with data collection techniques through interviews and document review. The research data was then analyzed in the phases of data reduction, data presentation, and conclusions. One of the indicators used is the collaborative process. The results of this research have shown that the collaboration process under collaborative governance did not operate optimally and was not fully effective, although several indicators were met, such as in building cooperation in the implementation of the prevention of violence against women and children In the city of Makassar, stakeholders meet regularly every quarter, particularly in the form of coordination meetings. In addition, cooperative governance is implemented with sustained commitment and the government participates in supporting violence prevention, particularly in the form of budget and infrastructure support. However, this research shows that there are still indicators that are not met and thus hinder the success of the cooperation. The regulations issued by the mayor are still in the finalization phase, and there is still an increase in violence against children. Based on the research results, the researchers suggest to the government the need for monitoring and evaluation to strengthen the role of the private sector, academia, business, NGOs, mass media, and society to enhance commitment and improve coordination between the realized.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".