Bibliographic record
Abstract
Sufism is the unique esoteric way to close to Allah as The Creator of this universe. Sufism teaches its scholars to purify their souls from the sins or something prohibited by Allah, and fulfill them with a good actions and characters. Sufism also coaches its scholars to base their actions with love to Allah, since love to Allah will guide scholars to love all Allah's creations such as like human, animal, tree, and all inside this universe. Loving Allah also drags people to be easy in obeying Allah's commandments where without love, people possibly hard to apply them. In all these terms, Sufism understood by its insiders (scholars) as a doctrine to be closer to Allah that must be obeyed. In opponent with these insiders, the outsiders acknowledge Sufism is mere tenet that not must be obeyed totally, but can be absorbed and adopted partly. Annemarie Schimmel is one of outsiders Sufism. Schimmel adopted al-Rumi's Sufism taught without becoming Moslem who al-Rumi becomes. Schimmel only looks Sufism from the outside. She judges Sufism as a mystical taught which not only found in Islam, but also in other religions which have a mystical method. That's why she felt not need to be a Moslem, even she adopted and applied al-Rumi's taught, because she did not view al-Rumi as a Moslem teacher but a mystical teacher. Even Schimmel was not a Moslem, but, her study to Sufism should be appreciated, because outsider typically objective in their study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".