« Il est difficile de se dire une personne. Et de dire quelle personne nous sommes1 » : l’autoportrait littéraire contemporain entre confession et dissimulation
Bibliographic record
Abstract
Cet article a pour objectif de penser la manière dont l’autoportraitiste en littérature répond à la grande question « Qui suis-je ? » — qui n’est pas sans favoriser la confidence — par une série de moyens détournés, faute de pouvoir se désigner par son corps : mention de son signe astrologique, portrait chinois, tests de personnalité, descriptions des vêtements, du décor dans lequel a lieu la scène de création de l’autoportrait. Il s’agira ultimement d’observer que le caractère que montre l’autoportrait n’est pas tant celui du référent (de la personne) qu’une représentation de ce caractère, c’est-à-dire un caractère pensé en fonction de l’idée qu’il va être vu, et façonné, en même temps, par les préjugés entourant son apparence.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".