Bibliographic record
Abstract
Aesthetic is concerned with beauty or the appreciation of beauty. It is used to talk about beauty or art and people’s appreciation of beautiful things. Its value can be defined as sensation or feeling of enjoying beauty. Every human will have the value of aesthetics or aesthetic sense that one get to enjoy whatever he sees in his daily life. As a study it aims at the understanding of beauty that is prescribed in front of us. It is not just expressing, enjoying beauty our inner emotions are connected to pursuit of beauty in all its means. In this article Dr. Naseer Ahmad Nasir view on Aestheticism are described who is a notable Islamic Scholar & philosopher from Pakistan. He wrote numerous books on aesthetics including Husn- e -Tafseer in three volumes. His important contribution to philosophy was research on Aesthetics. He tries to explore the Holy Quran with reference of Philosophy of Aesthetics. He claims that it is a unique effort because his work is matchless throughout the Muslim World. Actually, aesthetic is successful attempt to view and to discover the beauty of universe. Human being is part of its beauty and Holy Quran invites this creature to create a very beautiful society to live in. He tries to put before the world this aspect of Holy Quran.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.977 | 0.973 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".