Bibliographic record
Abstract
Dans ses œuvres de critique, mais aussi dans ses romans où les personnes réelles se muent en personnages, Françoise Lalande fait part de ses rencontres impromptues ou prolongées avec Jean-Jacques Rousseau, Arthur Rimbaud (et sa mère Vitalie Cuif), Vincent Van Gogh, Alma Mahler, Christian Dotremont, Germain Nouveau et d’autres. Loin de pratiquer l’hagiographie et particulièrement soucieuse de recréer l’atmosphère dans laquelle ils ont créé, Lalande entretient un commerce familier avec les artistes, les écrivains, les poètes surtout. Elle les piste sur tous les chemins qu’ils ont foulés, dans leurs œuvres, leurs tableaux, leurs poèmes, mais aussi dès qu’elle le peut dans la réalité de ce que fut leur vie. Elle cherche à les rencontrer partout, dans des lieux insolites ou de pure fiction, mais aussi dans les lieux réels où ils ont vraiment vécu.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".