Territoires émotionnels et engagement : un regard sur la rencontre et la relation homme-animal dans Le Lion de Joseph Kessel
Bibliographic record
Abstract
Through Le Lion (Gallimard, 1958), Joseph Kessel paints a fascinating fresco, where the skin and blood of men mingle with those of animals. Through the study of selected excerpts, we will analyze the means of encounter and communication between man and animal. We will see how literature and language, through the power of words and their effects, manage to engage the sensitivity of the reader - here a privileged witness - and to provoke the encounter with the animal-other, inscribing at the same time man and beast in common territories with signifying functions. The human-animal border is no longer thought of as a gap or a split, but as a passage, a plastic territory that becomes a dynamic and dynamizing function of a positive encounter, where identity is born from otherness and from a rediscovered dialogue with this other that resembles us so much and yet always escapes us.
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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 | 0.002 | 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 | 0.007 | 0.021 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".