Studying of Religious Common Themes in Seven Bodies of Nezamy and Eight Heavens of Amir Khusrau Dehlavi
Bibliographic record
Abstract
Seven bodies of Nezamy is rich trove from symbols, attitudes and different themes that with studying of them, in addition to understanding of global character of Nezamy and his deep thoughts, can went to the beliefs and religious infrastructures of poet. Amir Khusrau Dehlavi as the largest imitator of Nezamy had a particular attitude towards all aspects of seven bodies singing eight heaven. Both poets in singing of common concepts, have placed poetry as a means to express their high ideas. This idea expresses social situation of the age of every poet and on the other hand, it expresses the governing religious, doctrinal, moral principles on that time. In the present article it has been tried that most religious themes are extracted that they are most imitated and adapted themes in terms of terms, combinatory and the rhymes in the two system; they amount the frequency of them is been determined in each system. Also, the most obvious evidences of example are mentioned that it indicates imitation and similar writing. It is obvious in this study that innovative aspects and innovations of Dehlavi in eight heaven are also taken into consideration.
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 | 0.001 | 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 | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".