Bibliographic record
Abstract
يهدف هذا المقال إلى بيان مدى اتصال امرئ يقيس باليمن من خلال شعره، هل كان لليمن حضور بارز في شعره؟ وما أبرز مظاهر ذلك الحضور. امرؤ القيس يمني كندي، هاجر أجداده إلى شمال الجزيرة، وأسسوا مملكتهم هناك، لكنه ظل مرتبطًا بأصوله؛ فزار اليمن أكثر من مرة، وحضر اليمن في شعره إنسانًا ومكانًا وأشياء. وكان اليمنيون يعرفونه ويحفظون شعره قبل الإسلام. يؤكد امرؤ القيس على يمنيته هذه في شعره، فقد تغنى بملوك حِمْيَر، وحِمْيَر ذاتها، وتغنى ببطون كهلان وقبائل اليمن وأعرابها، وذكر مرابع آبائه ومراتع صباه في حضرموت في سياق الشوق والحنين، وذكر وديانًا وحصونًا ومدنًا ومخاليفَ يمنيةً قديمةً. ويمتد هذا الحضور اليمني في شعره إلى الأشياء اليمنية، أو المنسوبة إلى اليمن، فيذكر عددًا منها. الكلمات المفتاحية: امرؤ القيس، الشعر، اليمن. Summary: This article aims to show the extent of a person’s connection to Yemen through his poetry. Did Yemen have a prominent presence in his poetry? What is the most prominent manifestation of that presence? Imru' al-Qais is a Yemeni-Canadian. His ancestors migrated to the north of the island and established their kingdom there, but he remained connected to his origins. He visited Yemen more than once, and Yemen was present in his poetry as a person, a place, and things. The Yemenis knew him and memorized his poetry before Islam. Imru' al-Qais emphasizes this Yemeniness of his in his poetry. He sang about the kings of Himyar, and about Himyar itself, and he sang about the bellies of Kahlan and the tribes of Yemen and its Bedouins. He mentioned the pastures of his fathers and the pastures of his youth in Hadhramaut in the context of longing and nostalgia, and he mentioned ancient Yemeni valleys, fortresses, cities, and outposts. This Yemeni presence in his poetry extends to Yemeni things, or those attributed to Yemen, and he mentions a number of them. Keywords: Imru' al-Qais, poetry, Yemen. .
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.199 | 0.190 |
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".