Bibliographic record
Abstract
Listen," he said, not unkindly."If you want to talk theories we can have a lot of fun, but we shan't get very far.If you want me to admit that there are exceptions to my idea of justice, you can take it as admitted; but we can't go on from there without getting down to cases."Leslie Charteris, "The Invisible Millionnaire", In Follow the Saint, Londres, Pan Books, 1963, p. 103. 1Le Comte de Monte-Cristo, disparaissant sur son bateau au fin fond de l'horizon au terme du roman qui narre ses aventures, échappe à la justice des hommes -dont il s'est appliqué sur des centaines de pages à dénoncer l'insuffisance et la partialité -et ne paraît pas devoir se soucier outre mesure de celle de Dieu, personnage falot qui, en dépit de (r)appels rhétoriques, a surtout brillé dans le roman par son absence.Caroline Julliot, dans cette étude méthodiquement argumentée, nous convie cependant à l'imaginer au banc des accusés, trainé jusque-là par quelque Javert ou Vidocq hypothétique, dont l'obstination serait venue enfin à bout de l'astuce diabolique de ce personnage que, après Umberto Eco, on qualifie couramment, comme si cela allait de soi, de surhomme (en oubliant ou en minimisant parfois le fait que celui-ci avait repris le terme d'Antonio Gramsci, qui l'utilisait avec une bonne dose de sarcasme dans sa polémique contre le fascisme mussolinien, dans l'intention non pas de valoriser l'invention dumasienne, mais de dévaloriser la philosophie nietzschéenne).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.116 | 0.035 |
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".