Animal sauve enfant abandonné : quand la topique animalière vient éclairer autrement les topoï répertoriés (à partir du Shâhnâmeh de Ferdousi)
Bibliographic record
Abstract
The article continues with the post entitled "Poetic memory or the use of topos "ENFANT ABANDONNE" in Milan Kundera’s The unbearable lightness of being " published on the SATOR research notebook by Madeleine Jeay, in which she shows that Kundera works a topical situation corresponding to a number of topoi listed in SATORBASE as ADOPTER ENFANT TROUVE, DESOBEIR ORDRE DE TROUVER ENFANT, ENLEVER SECRETEMENT ENFANT etc. The article continues this reflection and focusses on the animal topic in the Shah Nameh of Ferdousi, reflecting on the role of the Simorgh (this mythical bird that plays an essential role in Persian culture and that is found in the Manteq-et-their (The dialogue of the birds) of Farid eddine Attar and in the metaphysical and philosophical texts of Avicenna and Sohravardi. In the Shah Nameh the Simorgh rescues Zal, abandoned as a child by his father, King Sam, and raises him until the latter, now an adult, finds his father who grants him the royal filiation. And the child is fed at night by a gazelle. Such topical configurations that could be called ANIMAL SAVES CHILD ABANDONS are very widespread. We question the original scene of a close relationship between human sovereignty and animal sovereignty, the part of sovereignty being equally distributed between man and animal which is here the inseparable adjuvant. The methodological question of topical naming is also addressed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".