The implementation of sharia bylaws and its negative social outcome for Indonesian women
Bibliographic record
Abstract
The formalisation of sharia law has been the subject of wide-ranging debate in Indonesia, also internationally. This is because this idea has significant implications, politically and socially, not only for Muslims, but also for women and other followers of other religions who live in Indonesia. It is important to note that there are 78 sharia bylaws which have already been ratified by regional authorities. And more than 52 cities and regencies have applied these regulations at the regional level. Some analysts argue that the implementation of sharia bylaws reflects on the fact that the majority of the Indonesian population needs morality and public order which will be beneficial for improving their lives. However, others rebut this argument by pointing to the fact that the enactment of sharia laws will discriminate and trigger violence against women. This paper will examine the implementation of sharia bylaws and its impacts on Indonesian women. This paper will argue that the implementation of sharia laws have negative impacts on Indonesian women because it has caused negative social outcome for women and women is the most vulnerable from this policy. Formalisasi hukum syariah atau penerapan perda Syariah telah menjadi topik yang menarik debat hangat di Indonesia, juga secara internasional. Hal ini Indonesian Journal of Islam and Muslim Societies karena kebijakan dan ide ini mempunyai dampak yang sangat serius –secara politik dan sosial—tidak hanya untuk kalangan Muslim, tapi juga untuk perempuan dan pemeluk agama lain di Indonesia. Penerapan perda Syariah hingga saat ini masih terus berjalan dan ada 78 Perda Syariah yang sudah diratifikasi oleh pemerintah lokal. Selain, lebih dari 52 kota dan kabupaten yang telah menerapkan Perda Syariah ini. Sebagian kalangan berargumen bahwa penerapan Perda Syariah adalah hal yang wajar karena mayoritas penduduk Indonesia adalah Muslim dan mereka membutuhkan aturan publik dan moralitas untuk kehidupan mereka. Namun, sebagian berpendapat bahwa menolak argumen tersebut dengan memberikan fakta bahwa perda Syariah akan mendiskriminasi dan memicu kekerasan terhadap perempuan. Artikel ini akan berargumen bahwa penerapan Perda Syariah memberikan dampak negatif terhadap perempuan karena ini mengakibatkan dampak sosial yang buruk terhadap perempuan dan perempuan menjadi pihak yang paling rentan menderita dari kebijakan ini.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".