Comparative Study on Sir Thomas More & Hakim Abolqasem Ferdowsi, in Subject & Utopia
Bibliographic record
Abstract
The present study compares two poets and scholars (English and Persian) in terms of subject and utopia. Sir Thomas More’s Merry Jest is compared with one piece of Ferdowsi’s Shahnameh in terms of the common subject or message they convey in classification of people’s occupations. Having a civilized and more disciplined society, both poets believed in classifying people based on their skills, competence and efficiencies; and insisted that each group should remain in their own category and avoid interfering or entering the profession of which they know nothing. Moreover, as social scholars, both put forward the theory of utopia and describe the ideal society in which people can live more comfortably and pleasantly. Living in the 16th century, the principles proposed by More for his utopia basically turn round modern social interactions and attempts to recognize the reason of problems at the first step, and then amending them through the laws he suggests. In Ferdowsi’s utopia, however, the ideal society is based on two distinct factors: physical structure of towns, the number of necessary architectural buildings constructed, and the moral enhancement of its residents in holding high human values as honesty, integrity and knowledge. The present research aims to probe, examine and find answers for two main questions: what affinities and dichotomies are there in “job classification” and the concept of “utopia” held by Ferdowsi & More? The research method is library-based and the obtained results are categorized by descriptive-analytic method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".