Ces immortels compagnons de nos enfances : Ressource du site Livres d’enfants d’hier et d’aujourd’hui
Bibliographic record
Abstract
Fiche pédagogique téléchargeable au format PDF. L’univers de l’enfant est peuplé de personnages imaginaires, doubles de lui-même, qui l’entraînent dans un monde merveilleux, ou le font voyager par des chemins semés d’obstacles. Sages ou fous, appliqués ou rebelles, héros victimes ou anarchistes en rupture de ban, tendres, abandonnés ou intrépides sans concession, désireux d’intégration ou poètes d’une langue imaginative et joueuse, c’est dans un très large spectre d’attitudes que se déploie la fresque des personnages de la littérature de jeunesse. À chaque génération ses favoris ? Pas toujours… Nombreux sont ceux qui enjambent les époques, remis au goût du jour par l’illustration et souvent adaptés en bande dessinée, film ou dessin animé. Le héros passe alors devant son auteur : on connaît Robinson, mais quel enfant lit encore Daniel Defoe ? Et Mark Twain, Kipling, Lewis Carroll, J.-M. Barrie, Hector Malot ?… Robinson, Tom Sawyer, Mowgli, Alice, Peter Pan, Rémi sont devenus des archétypes et n’en finissent plus d’engendrer des avatars.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.010 |
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".