Efforts to Protect Violence in the Households during Covid-19 in Indonesia
Bibliographic record
Abstract
The social distancing appeal that the government encourages is not matched by the state's efforts to provide economic security to the community. PSBB will directly or indirectly limit the movement of the community. The teaching and learning process at schools and residents who work will be limited to working or studying at home. This limitation of activities in public spaces will have an impact on people's income, especially those in the middle to lower economy. The implementation of social distancing during the Covid-19 outbreak has increased the risk of violence against women, complicates women's economic conditions, and affirms women's social status as subordinate, or women are in a lower position than men. The formulation of the problem in this research is what is the cause of domestic violence during the covid-19 period in Indonesia, what are the prevention efforts against domestic violence during the covid-19 period. Causes of Domestic Violence During the Covid-19 Period, namely the government's appeal to the community “at home alone”, causing a separate polemic for women and children, especially those who experience economic and psychological pressure at home from extraordinary isolation measures, has prompted increasing instances of reports of domestic violence, especially women who are forced to live for months in abusive relationships. causes and consequences of violence and to prevent the occurrence of violence through primary prevention programs, policy intervention and advocacy as well as information programs and supporting initiatives through all mass media TV, social networks, cell phones.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".