Addressing Campus Sexual Violence: A Collaborative Governance Approach to Legal Policy
Bibliographic record
Abstract
Campus sexual violence is a pervasive issue affecting institutions worldwide, with a concerning upward trend in reported incidents. This article scrutinizes legal policies and the efficacy of collaborative governance in mitigating this pervasive problem. The model of collaborative governance, a globally recognized legal policy approach, is at the heart of this study. Utilizing socio-legal research methods, the article employs comparative legal and conceptual legal approaches, drawing from secondary literature sources. The study reveals that the implementation of legal policies and collaborative governance can significantly curb campus sexual violence in Indonesia. This can be achieved by actively engaging both academic community and the wider public. By drawing lessons from countries such as the United States, the Netherlands, Canada and the United Kingdom, Indonesia can replicate their success in reducing campus sexual crimes. These measures include the creation of legal policies, establishing partnerships with central and local governments, and fostering collaborative governance. Key initiatives that have proven effective in these countries include the development of service guidelines that prioritize victim and survivor protection, amplified anti-sexual violence campaigns on campuses, and the promotion of sexual violence awareness education. The establishment of a National Campus Sexual Assault Commission to evaluate the enforcement of educational ministerial regulations and religious affairs ministerial regulations in preventing and addressing campus sexual violence is also recommended. This article offers valuable insights for policymakers in their efforts to devise effective legal strategies to prevent sexual violence on campus.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".