Bibliographic record
Abstract
Dans « Patrimonialiser les bibliothèques d’écrivains : le cas des Archives & Musée de la Littérature », Laurence Boudard aborde la question de la conversation des bibliothèques pour les institutions. Après avoir contextualisé l’étude des bibliothèques et avoir interrogé la position d’une institution consacrée à la mémoire littéraire, l’article réfléchit à partir du cas des Archives et Musée de la Littérature (AML) à Bruxelles et des bibliothèques d’auteurs qui y sont conservées, à la gageure logistique que représente la conservation d’une bibliothèque d’écrivain. L’auteur aborde alors les enjeux et problématiques de la bibliothèque vue comme un patrimoine en soulignant qu’une bibliothèque est un organisme vivant, un objet hétérogène, entité dynamique, parfois dispersée entre plusieurs institutions, objet d’une conservation sélective. Puis l’article discute la question du dispositif des cabinets d’écrivains comme tentative de restituer le contexte d’origine de la bibliothèque, à partir de l’exemple du cabinet de Verhaeren, et en souligne les limites.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".