THE ADAPTATION OF CYBER SALAFISM DAʿWAH IN HADĪTH AḤKĀM LITERATURE: ANALYZING ʿUMDAT AL-AḤKĀM SERMONS ON THE YUFID CHANNEL
Bibliographic record
Abstract
This article examines how Salafism adjusts conservative literature with its preaching on YouTube. Over the last decade, Salafism has gained significant traction in Indonesian cyberspace and urban centers. Moreover, the impact of expanding preaching through social media platforms, such as Instagram, YouTube, and Facebook, has proven successful in increasing their popularity in urban areas. Therefore, uncovering the modification of conservative law literature in cyber-Salafism is significant because it not only captures the method of Salafism in online preaching but also reveals how Islamic classical books are adapted for online media. The object of this study is 240 Yufid videos discussing ʿUmdat al-Aḥkām. Therefore, this study employs an online ethnographic method in order to collect, classify, and analyze the data. Moreover, it is also applied to defining the setting of the research, feeding insight to the studied community, and presenting the results with ethical awareness. This study revealed that Yufid’s interpretation of ʿUmdat al-Aḥkām tends to be textual and seems indifferent to the social conditions of the surrounding community. Moreover, it also uncovered how ʿUmdat al-Aḥkām sermons manifest the business purpose of Yufid rather than theological demand. Through this research, we suggest that the government or scholars can monitor online preaching on YouTube channels as an extension of real movements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".