How Is the Language of Imagery in lyric’s of Amir Khosro Dehlavi
Bibliographic record
Abstract
Whenever there is an amazing resurrection in a nation’s language which leads to create an artistic masterpiece in that language, it is called literature. For this reason, we can differentiate between literary language and daily or spoken language. Writers and literates from various countries will often try to utilize a literary approach in order to make their utterances and poems more attractive and popular through the use of written imagery and artily, illustratively capturing the emotion of the written words. Amir Khosro Dehlavi; who is a well-known Hindi poet, has mastered the illustrative nature of the Farsi language to compose his euphonious lyric poems. In reviewing his work, we will try to evaluate the context of his poems and the way he has used imaginary and artily language structures, consisting of words, combinations, sentences and phrases in his lyrics, as well as evaluating the way he introduces an imagery approach to his poems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".