Upaya Indonesia Mengurangi Eksploitasi Seksual Komersial Anak (ESKA) di Industri Pariwisata dalam Mewujudkan Tujuan Pembangunan Berkelanjutan 2030
Bibliographic record
Abstract
This article highlights the Indonesian Government's efforts to reduce Commercial Sexual Exploitation of Children (CSEC) in the tourism industry as a realization of supporting the sustainable development agenda, in order to create a society that is more alert to sexual violence against children. CSEC in the tourism industry usually involves perpetrators from foreign and domestic tourists who place children in physical and mental sexual harm. This issue affects the ability of national governments to protect their citizens in the context of sustainable development. Through the lens of international regimes and human security, this article reveals two main points. First, the Indonesian Government's efforts are in line with the mandate in the Optional Protocol to the Convention on the Rights of the Child on the sale of children, child prostitution and child pornography (OPSC). Second, the Indonesian Government, led by the Ministry of Women's Empowerment and Child Protection (KPPPA) together with the Ministry of Tourism and the Directorate General of Immigration, the Ministry of Law and Human Rights of the Republic of Indonesia, has a series of national programs/plans to reduce Commercial Sexual Exploitation of Children (CSEC) in the tourism industry, namely (1) the declaration of Child Friendly Districts/Cities; (2) creation of child-friendly rural tourism free from exploitation; (3) establishment of a Community-Based Integrated Child Protection network; (4) stipulation of PP No.70 of 2020 concerning Chemical Castration; and (5) tightening the rules for entry and stay permits for foreigners. This series of efforts carried out by the Indonesian government can direct, shape mindsets and sensitize the Indonesian people to combat CSEC in the tourism industry in order to achieve sustainable development by 2030.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".