Functions of Social Traditions in Tendency of Polytheists of the Arabian Peninsula to Islam (Case Study: Ethnic and Tribal Relations)
Bibliographic record
Abstract
There have been traditions and customs among all peoples in the past so far. What is important is Functions of social traditions that it can be assessed as such how it would be used. Societies that do not tend to social changes use the traditions on the regressive path; but it does not mean that the traditions always so resist stubbornly against the reform and modernization. But if the functions of tradition replaced in the direction of reform and social changes, its positive functions will be used. Hence, the Prophet of Islam in the ad of Islam not only did not take action to remove the prevailing traditions but also used its positive functions. In this article it is argued that: How Functions of social traditions have been in tendency of polytheists of Arabian Peninsula to Islam? In response to this question, the main claim is as follows: positive and negative Functions of social traditions in the form of content and quantity, have had significant effect in individual and quite a few cases group tendency of polytheists of Arabian Peninsula to Islam. Access to this entry that the customs and traditions prevalent among nations and peoples are not only constitutive of the past but in line with reform and social changes can also be used form their positive functions; including the achievements of research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".