Figures de l'envoûtement. L'exemple de <em>La Mort à Venise</em> de Thomas Mann
Bibliographic record
Abstract
Tout peut servir de base à une figure, tout peut envoûter. Que ce soit une femme aperçue à une fenêtre, un objet fétichisé, une partie de sa propre anatomie, son nez ou ses mains. Dès l’instant où un objet est doté de signification, cette transfiguration lui attribue une aura qui n’est autre que le signe de sa désirabilité. Il s’agira dans cet article de s’arrêter à un exemple d’envoûtement, celui mis en scène dans La Mort à Venise de Thomas Mann. Gustave Aschenbach, l’écrivain mis en scène, tombe sous le charme de Tadzio, un jeune homme qui l’envoûtera, sans jamais se douter du rôle qu’il joue dans ce drame. Cet envoûtement ne sera pas passager, mais mortifère, car l’obsession pour Tadzio ira jusqu’à entraîner Aschenbach dans un état de perdition total, l’écrivain se laissant mourir dans une Venise infestée par le choléra asiatique. Je me servirai de cet exemple pour illustrer les mécanismes par lesquels nous nous dotons de figures.AbstractAnything can serve as the basis for a figure, anything can be spellbinding. Whether a woman seen at a window, a fetishized object, part of our own body, our nose or our hands. The moment an object has acquired some meaning, this transfiguration gives it an aura that is just the sign of its desirability. In this article, I will look at an example of bewitchment, the one staged in Thomas Mann’s Death in Venice. Gustav Aschenbach, its hero, a writer in decline, falls under the spell of Tadzio, a young man who never suspects the role he plays in this drama. This obsession with Tadzio will lead Aschenbach to a state of total destruction, leaving the writer dying in a cholera-infested Venice. I will use this example to illustrate the mechanisms by which we create and give life to figures of the imagination.
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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 | 0.000 | 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 | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.018 |
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".