REVISITING A FRAGMENT OF THE EARLY HISTORY OF ISLAM: THE MYTH OF THE ORDER TO ASSASSINATE ENEMIES BY THE PROPHET
Bibliographic record
Abstract
This article studies a part of early Islam's history that seems ambiguous due to some reports. According to these reports, Prophet Muhammad issued decrees ordering of his opponents and enemies' assassination. The qualitative method has been used in the article. The article shows that there are only 4 cases that appear to have been terrorist operations. Another achievement of the article is that among these 4 cases, only one seems to be valid, and the other three are all rejected because of their chain of narrators. Also, these 4 cases, have all occurred on the battlefield and warzone, and none are indicative of terrorist operations. The article also argues that in the life-history studies of the Prophet, the basic principles and general teachings of the Prophet must be our foundations. The article refers to a general principle in the teachings of the Prophet, according to which He proclaimed that a Muslim must never assassinate. In conclusion, neither the authenticity of the reports, nor their texts are acceptable; they are contrary to the general policy of Islam in the fight against terrorism. The article suggests that the general principles and central teachings of the Prophet should be of interest to researchers in the study of early Islam history, which sometimes contain inconsistent and ambiguous reports.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".