Pendekatan Munasabah Psikologiah Muhammad Ahmad Khalafullah: Analisis Kisah Luth dan Kaumnya dalam Al-Qur’an
Bibliographic record
Abstract
One of the most important ways the Koran uses to convey the messages of God is to use stories. In the Qur’an, the mention of stories fulfills a quarter of the number of verses; there are recorded 1,453 to 1,600 verses. One of the stories in the Koran is the story of Lut and his people. The purpose of the present story is as a guide, warning, threat to humans. But some commentators have been ‘fascinated’ by revealing the reality of historical events, thus leaving the essence of the meaning contained. Khalafullah, in contrast to the others, tried to study the stories in the Koran by using literary methods. In his method, he revealed a psychological muna>sabah between stories and the conditions of the prophet or society when the Qur’an was revealed. This research found that, according to Khalafullah, the stories used by the Koran as an effective way to attract the interest and attention of Muslims at that time; found the relationship between the story of Lut and his people with the psychological condition of the Prophet and his followers, and there is a relation between the story of Lut and the psychological atmosphere of the Prophet’s opponents at that time. This research reinforces the theory that the context of the Prophet and his people when the Qur’an was revealed is a very important element to consider as someone tries to understand the message of the Qur’an.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".