Challenges for a Local Service Agency to Address Domestic Violence –A Case Study From Rural Indonesia
Bibliographic record
Abstract
Since the launch of a Zero Tolerance Policy in Indonesia, several policies to address domestic violence have been enacted. The obligation of local governments to establish service units for women survivors of domestic violence is one of them. Since domestic violence is a sensitive and complex issue in Indonesia it is important to understand how governmentally regulated services function in practice. This case study aimed to explore challenges faced by a local service agency in managing service provision for women survivors of domestic violence in rural Indonesia. Data from one focus group discussion (12 participants), four individual interviews, six short narratives, two days of participant observation, as well as archive reviews were collected. All data were analyzed using Grounded Theory Situational Analysis. The major challenge faced by the local agency was the low priority that was given them by the local authorities, mirrored also in low involvement by the assigned volunteers in the daily service. The study also identified a gap between the socio-cultural arena and the law & policy arena that needs to be bridged to avoid that the two arenas address domestic violence in a contradictory way. Budget allocation to support the sustainability of the daily routines of service agencies has to be given priority. There is also a need for careful considerations regarding the composition of personnel involved within daily management of service agencies addressing domestic violence. To bridge the gap between the legal systems and traditional cultural values, culturally adjusted alternative justice systems could be developed to increase women's access to legal support.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".