Bibliographic record
Abstract
Shahnameh as one of the most important literary works that reflects the pure thoughts of the past Iranians, plays a key role in preserving the Iranian cultural heritage and national identity. Mythology helps us to understand the civilizations included the cultures. For example, the image of the women in the literary works is different from their modern popular image that ignores the real position of the women. Abu l-Qasim Ferdowsi, the highly revered Persian poet, is one of the literary figures who considered the role of women in his literary masterpiece in spite of the prevailing attitudes towards the women in his era. Some studies, due to the lack of understanding the Ferdowsi’s poems, have claimed that he is a misogynist poet. However, Ferdowsi has equally ranked men and women in his long epic. For example, there are a number of chaste and compassionate mothers in Shahnameh, who play a vital role in shaping the epic character of the heroes. Or, the women who fall in love and relinquish all her possessions for the sake of fruition, such as Manijeh and Katayun who leave the king’s court for the sake of love. Ferdowsi’s imagery of women is not a descriptive account of their charming superficial beauties, but it reflects the wisdom, bravery, and belligerency of these women.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".