PENGISLAMISAN SEJARAH ISLAM DI NUSANTARA DALAM CERAMAH USTAZ SALIM A FILLAH
Bibliographic record
Abstract
The article focuses on the lectures of Salim A Fillah (SAF), a popular preacher known for his Islamist activism within the Prosperous Justice Party (PKS) in Indonesia. The data is obtained through digital observation and literature review. The analysis employs the Roland Barthes semiology method to examine SAF's lectures on the history of Islamization in Nusantara, which are widely available on platforms like YouTube. The denotation stage of the analysis involves SAF dividing the history of Islam's arrival in the archipelago into seven waves, each characterized by distinct discourse. However, during the connotation stage, it is revealed that SAF's periodization of Islamic history is framed by a narrative of Islamism. This means that SAF portrays the history of Islam in the archipelago as the history of Islamism. The article suggests that SAF implicitly preaches that the saints who spread Islam in the archipelago were individuals with missionary missions similar to present-day Islamists in Indonesia. Additionally, the article highlights SAF's emphasis on transnational Islamic movements like HTI and the Muslim Brotherhood, which are associated with the PKS, as continuations of the missionary efforts of these saints. Therefore, it concludes that SAF has constructed an Islamist narration of Islamic history in Indonesia. SAF also blurred the lines between historical facts and ideological storytelling
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.155 | 0.035 |
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".