Bibliographic record
Abstract
Y a-t-il ici quelqu'un qui prétende s'appeler Don Quichotte de la Mancha?S'il ose supporter le poids de mon regard qu'il avance.-Je suis Don Quichotte de la Mancha, chevalier à la triste figure.-Écoute-moi, charlatan, tu n'est pas un chevalier mais un dérisoire imposteur.Tes jeux ne sont que des jeux d'enfants et tes principes ne valent guère mieux que la poussière qui rampe sous mes pieds.-Manque de courtoisie, fausse chevalerie, donnemoi ton nom avant que je te châtie.-Arrête, Don Quichotte!Tu voulais mon nom, je vais te le dire, je m'appelle le Chevalier au Miroir.Regarde, Don Quichotte, regarde dans le Miroir de la réalité, regarde, que vois-tu, rien qu'un vieux fou.Regarde, regarde.Plonge, Don Qui chotte, plonge, viens te noyer en lui, et il est l'heure de couler, la mascarade est terminée, avoue que ta noble Dame n'est qu'une putain et que ton rêve n'est que le cauchemar de l'esprit qui s'égare.-Je suis Don Quichotte, chevalier errant de la Mancha, et ma noble Dame est Dulcinea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".