Bibliographic record
Abstract
الحمد لله رب العالمين والصلاة والسلام على أشرف المرسلين سيدنا محمد النبي الأمي، وعلى آله الطيبين الطاهرين ورضى الله عن الصحابة أجمعيـن والتابعين لهم بإحسان إلى يوم الدين، وبعد،،،، فهذا بحث بعنوان (القرآن الکريم والسنة النبوية في مواجهه الانحرافات) تناول فيه الباحث قضية الانحرافات الفکرية والسلوکية وکيفية مواجهتها والتصدي لها من خلال القرآن الکريم والسنة النبوية، وقامت البحث ببيان مفهوم الانحراف وأنواعه، وأهم أسبابه وأبرز مظاهره، ثم بدأت تضع علاجا لتلک الانحرافات، وقد استخدمت في ذلک کله المنهج الاستقصائي والاستنباطي، من خلال نصوص القرآن الکريم والسنة النبوية الکلمات المفتاحية: القرآن الکريم - السنة – الانحراف- التحولات . The Holy Qur’an and the Sunnah of Prophet Muhammad (Peace be upon Him) Facing Transgression By: Prof. Seif Rashid Al-Gabry Professor of Culture and Society The Canadian University in Dubai & Al-Sharjah Heritage Institute, UAE Saif2546@yahoo.com Abstract This research is entitled {The Holy Qur’an and the Sunnah of Prophet Muhammad (Peace be upon Him) Facing Transgression}. The researcher has tackled the issue of intellectual and behavioral transgressions and how to face them aided by the Holy Qur’an and the Sunnahof Prophet Muhammad (Peace be upon Him). The researcher has displayed the concept of transgression, its types, its causes and its most apparent symptoms. Next, the researcher has prescribed a remedy for such transgressions. She has followed the deductive and inductive approaches through the texts of the Holy Qur’an and the Sunnah.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.164 | 0.107 |
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".