Biodiversity Information System for Management of Medicinal Plants Data Tropical Rainforest Borneo
Bibliographic record
Abstract
This article exposes the fundamental mistake of Afrizal Nur (AN) in criticizing Tafsir Al-Mishbāh by Muhammad Quraish Shihab (Shihab). This study was conducted considering that AN had been very brave in criticizing Tafsir Al-Mishbāh. Even though it is wrapped in the highest academic work (dissertation) and published as a book, it is complex not to be called less careful in correcting Shihab. This widely spread work eroded some people's trust in Shihab's intellectuality and integrity. Unfortunately, no academic work has tried to address this issue, let alone published in scientific journals. This article is the result of a text analysis study. The criticism of AN is studied so that it is compared to Shihab's original work and considered based on related theories and conclusions. It was found that AN made a fundamental mistake in managing its data. His data breaks down some of the findings used as criticism of Shihab. AN often equates Shihab's opinion with other interpreters' views in his commentary. When it finds the opinion of Thabāthabā'ī, for example, the AN without hesitation refers to it as the opinion of Shihab, even though Shihab has written a different opinion, even refuting it. AN even seems to have difficulty grasping the meaning intended by Shihab, so it comes to a different conclusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".