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Record W2529896070 · doi:10.5539/ass.v12n11p46

Studying of Religious Common Themes in Seven Bodies of Nezamy and Eight Heavens of Amir Khusrau Dehlavi

2016· article· en· W2529896070 on OpenAlexvenueno aff
Reza Ashrafzade, Shabnam Shafiey Lotfabadi

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldPsychology
TopicFamilies in Therapy and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsHeavenImitationSingingPoetryCharacter (mathematics)LiteratureAestheticsPsychologySociologyPhilosophyEpistemologyHistorySocial psychologyArtMathematics

Abstract

fetched live from OpenAlex

<p>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.</p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.322
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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