Bibliographic record
Abstract
Résumé: Pour enseigner ce roman de 1890, on utilise des pages du Dossier préparatoire de Zola, l'article "Tempérament" du Grand Dictionnaire universel du XIXe siècle, des critiques variés du roman, un article sur les codes en conflit dans le roman, des notes sur la théorie descriptive, quelques images liées aux roman, un dessin de C. Bertand-Jennings, et deux extraits audio du roman. A part des explications de texte orales, et des exposés oraux (puis rendus écrits), on traite l'illusion du réel, le renversment dans le roman des données quasi-scientifiques, et une nouvelle approche à la lecture de description. Resumé: To teach this 1890 novel, we use pages from Zola's Dossier préparatoire, "Tempérament" from the Grand Dictionnaire universel du XIXe siècle, various critics of the novel, an article on codes in conflict in the novel, notes on descriptive theory, a few pictures linked to the novel, a sketch by C. Bertrand-Jennings, and two audio clips. Beside oral textual analyses, and oral presentations (handed in later written), we treat the illusion of the real, the novel's reversal of its quasi-scientific givens, and a new approach to reading description.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".