THE ROLE OF MUSLIM WOMEN \nIN PREVENTING VIOLENT EXTREMISM (PVE) IN INDONESIA
Bibliographic record
Abstract
Within the discourse of Islamic extremist movement, Muslim women are no \nlonger seen as supporters. There are cases in which women are behind the \nviolent action. Between 1985 and 2010, female bombers committed over 257 \nsuicide attacks (representing about a quarter of the total). In 2017 there are 420 \nIndonesian returnees from Syria who joined ISIS, 70 percent of them women and \nchildren. At least 45 Indonesian women migrant worker has suspected involved \nat ISIS. Social Media have a big contribution for recruiting the extremist member. \nThe power of social media is to influence netizen by emphasizing feeling instead \nof thinking which reinforces gender stereotypes that women are more emotional \nthan rational. \nAlthough more women have been actively involved in intolerant activities \nrecently, women’s roles as policy shapers, educators, community members and \nactivists in Countering Violent Extremism (CVE) have started to be recognized. \nWomen, Peace and Security (WPS) agenda from UNSCR 1325 also asserts that \nwomen's role in CVE is significant important. There is a strong correlation \nbetween gender inequality and the status of women and violent conflict. \nPromoting gender equality is included in the recommendations in the UN’s \nPreventing Violent Extremism Plan of Action. Violent extremism is most \neffectively countered through increased education, better critical thinking and \nenhanced opportunities for women. There are at least 23 organizations in \nIndonesia contribute to a national CVE strategy for Indonesia. One of them is \n'Aisyiyah, a woman's wing organization of Muhammadiyah, one of two biggest \nIslamic organizations in Indonesia. This paper discusses the experience of \n'Aisyiyah promoting Peace of Islam through training which encourages women \nto be an agent of active tolerance. 'Aisyiyah implements active learning and uses \nmedia such as religious animation and poster as a training strategy.
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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 | 0.001 | 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 | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".