Bibliographic record
Abstract
Les armoires de nos grands-meres debordaient de linge en lin, metis et coton damasse : nappes et serviettes, torchons et napperons finement brodes, draps et taies d'oreillers sentant bon la lavande, bordes de jours et personnalises de superbes monogrammes. Sylvie Perrot-Humbert nous ouvre les portes de sa maison de famille et nous montre comment revisiter ce beau linge et realiser des objets d'aujourd'hui. Debordant d'idees, elle nous apprend a transformer un grand drap ajoure en housse de couette ou en tour de lit de bebe, une serviette en abat-jour ou en set de table, une taie d'oreiller en pochon ou galette de chaise... Les techniques utilisees - couture, teinture, transfert, cartonnage, collage, montage d'abat-jour - sont simples et tres clairement expliquees etape par etape. Les patrons taille reelle des abat-jour sont fournis en fin d'ouvrage.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.135 | 0.056 |
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".