Ruda Pants / Wan Anis Sofiyya Wan Mohd Hamdi and Siti Nur Syafiqah Mohd Nadzri
Bibliographic record
Abstract
Sarah Fuad is an online business that started on Facebook. It was established at the fourth quarter in the year 2021 and is focusing on Muslimah-friendly women’s clothes which are pants. Muslimah-friendly clothes are the most essential thing for Muslim women in Malaysia as it is our responsibilty as a muslim women to follow the islamic syariah that told us to wear a proper clothes to protect our aurah. Sarah Fuad is a partnership business that offered various types of women apparel but we only focus on selling pants. The main reason of this business is to provide Muslim-friendly pants for muslim women. The pants that we produce have followed Islamic Syariah criteria in how Muslim women must wear the clothes by following the restrictions that are made for Muslim women. Items that we sell is Ruda pants that are Muslim-friendly and have loose straight cutting. Our pants are not too tight like the other shop sells their pants/jeans that are too tight,where it shows the shape of our legs. Sarah Fuad is targeting the segmentation that consist Muslim women only. Some of the Muslim women have difficulty finding the right pants as most pants and jeans that were sold in the market does do fulfill the Islamic criteria in women’s clothing. Therefore, we wanted to help all Muslim women who want to be a Muslimah woman out there in providing the best clothes that is suitable and specially made for Muslim women Sarah Fuad is now runnning a Fcaebook account to widen its business platform while reaching a broader audience. It is showed that Fcebook is the best social media platform in promoting their business while communicating with the customers. This could help the seller and the customers build a good relationship with each other. Using this social media platform, we both set up our marketing campaign by posting seven teasers, 16 postings of hard sell and 16 postings for soft sell. We applied all sorts of ways to fulfill the hard sell, AIDCA method and soft sell, TISCta method, by writing a caption on the post and some of the graphics we design it by ourselves by using Canva.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.140 | 0.041 |
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".