Bibliographic record
Abstract
Are they any animal topoi, or are they merely pre-existing topoi that include animals through anthropomorphisation? The aim of this paper is to extend Sator's reflections on the birdsongs of the fables, which are at once zoological observations, toposemes and typemes. The analysis will focus on the Roman de Renart, the Fables de La Fontaine and the Scènes de la vie privée et publique des animaux, three sets of short narratives in which the animals at the centre are sometimes the narrators. There are several reasons for this choice of texts: the variety of narrative situations and species represented, both wild and domestic, and a certain homogeneity in the ideological context of each work. After a preliminary survey of the first Satorbase, I will analyse the narrative and topical treatment of situations specific to animals (eating, noises, migrations) before observing the most frequent topoi (such as tricking the trickster, hunger, an assembly of animals chooses a king or a representative), their narrative configurations and any variations. This topical examination will provide an opportunity to consider the status of animals in society: do the topoi reflect these changes? do they incorporate new animals? are they influenced by the gradual emergence of pets in the bourgeoisie?
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".