Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAuthorship Methodology in the Biography of the Prophet The Pioneers Stage
مناهج التأليف في السيرة النبويّة - مرحلة الروّاد
1573
1 29
0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تعدُّ السيرة النبوية من أقدم المؤلفات في التراث العربي الإسلامي المدوّن, و هي تكتسب أهميتها التاريخية بوصفها أسفاراً توثّق لحياة النبي الكريم محمد ( ص ) من جهة, و تنبئ عن ملامح الفكر العربي في بدايات مرحلة التأليف و الكتابة المنهجية من جهة أخرى. و يسعى البحث إلى تأصيل مصطلح السيرة النبوية, مناقشاً أسباب التأليف فيها, و مصادرها, و إرهاصاتها, ثم يقف البحث على أوّلية التأليف في السيرة النبوية من خلال دراسة الكتب التي خلّفها الروّاد في هذا المجال, مستكشفاً مناهج التأليف في خمسة نماذج منها, سعياً للوصول إلى نتائج تجلو دور هؤلاء الرواد في تثبيت ركائز التأليف المنهجي, و تطوّره, و دوره في التأسيس للكتابة في فنون التأليف الأخرى.
The prophet's bibliography is considered as one of the oldest Islamic literature works. These works are historically significant for two reasons. On the first hand, they are seen as manuscripts documenting the life of the Holy Prophet Muhammad (peace is upon him). On the other hand, they predict for the Arab thought methodology in the outsets of entering the field of writing and methodological authorship. This research focuses on the concepts of the prophet's bibliography, the reasons behind writing it, its resources and its harbingers. Moreover, it studies the content of five books exploring the writing methodology in them. These books, which are considered as the first stage of this type of writings, have been chosen in an effort to gain access to the results that show the authorship methodology in the case of the pioneers. In addition, this research highlights their role in establishing for writing in other types.
References used
العمري, د.أكرم ضياء, السيرة النبوية الصحيحة, مكتبة العلوم و الحكم, المدينة المنورة, ط 5, 1993م.
نصار, د.حسين, نشأة التدوين التاريخي عند العرب , منشورات اقرأ, بيروت, لبنان, ط 2, 1980م.
rate research
Read More
This research highlights two main approaches that are used in the most distinguished literary books in the Hijri third century:
The first one is digression. This approach is based on the spontaneous transferring of the readers from a subject or idea
to another without a logical connection. This approach may include a hint of confusion and a lack of methodicalness. It may also hide denotations, intentions and viewpoints that could not be made explicit. Al-Jahez (255 Hijri) is the most prominent representative of the approach in his two books (Al-Hayawan) and (Al-Bayan and Altabyeen), Al-Jahez is truly considered the real founder of digression.
The second one is the approach of categorization and classification that is based on arranging the cognitive, homogeneous vocabulary and putting them within one chapter with a specific title. In his book (Uyoun Al-akhbar), Ibn Qutaibah is considered the first to begin this method by dividing his book into categories. Each of these categories include a set of partly homogeneous news. This is because he does not want to completely break away from the tradition of digression used by his predecessors. Ibn Qutaibah's work was a leading stage in the field of categorizing and classification. As for the book (Al–Kamel) by Al–Mubarred; in spite of dividing this book to chapters, it does not hold the essence of categorization, and the division was just formal.
The Arabic authorship movement developed during the fourth century AH in a great
way, especially in methodological terms. The research aims to study one aspect of this
movement, which is the authorship in the books of thehadithrepresentative. It presents
the most important types of books in this field: books of names, Wound modification,
booksbiographies and news.
Determining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods. Recently, authorship representations learned using neural networks have been found to
outperform alternatives, particularly in large-scale settings involving hundreds of thousands of authors. But do such representations learned in a particular domain transfer to other domains? Or are these representations inherently entangled with domain-specific features? To study these questions, we conduct the first large-scale study of cross-domain transfer for authorship verification considering zero-shot transfers involving three disparate domains: Amazon reviews, fanfiction short stories, and Reddit comments. We find that although a surprising degree of transfer is possible between certain domains, it is not so successful between others. We examine properties of these domains that influence generalization and propose simple but effective methods to improve transfer.
Recent research has documented that results reported in frequently-cited authorship attribution papers are difficult to reproduce. Inaccessible code and data are often proposed as factors which block successful reproductions. Even when original mater
ials are available, problems remain which prevent researchers from comparing the effectiveness of different methods. To solve the remaining problems---the lack of fixed test sets and the use of inappropriately homogeneous corpora---our paper contributes materials for five closed-set authorship identification experiments. The five experiments feature texts from 106 distinct authors. Experiments involve a range of contemporary non-fiction American English prose. These experiments provide the foundation for comparable and reproducible authorship attribution research involving contemporary writing.
Authorship attribution is the task of assigning an unknown document to an author from a set of candidates. In the past, studies in this field use various evaluation datasets to demonstrate the effectiveness of preprocessing steps, features, and model
s. However, only a small fraction of works use more than one dataset to prove claims. In this paper, we present a collection of highly diverse authorship attribution datasets, which better generalizes evaluation results from authorship attribution research. Furthermore, we implement a wide variety of previously used machine learning models and show that many approaches show vastly different performances when applied to different datasets. We include pre-trained language models, for the first time testing them in this field in a systematic way. Finally, we propose a set of aggregated scores to evaluate different aspects of the dataset collection.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
