Bibliographic record
Abstract
Plusieurs romans d’Edmond et Jules de Goncourt (En 18.., Charles Demailly, Renée Mauperin, Manette Salomon) proposent de longues et riches descriptions de la Seine, que nous examinons afin d’en dégager la poétique commune, tout en relevant quelques exceptions marquantes. Les Goncourt interrompent volontiers la trame narrative d’un roman pour se complaire dans des compositions tout en nuances de couleurs, en effets de lumières et d’ombres, en contrastes de mouvements et en orchestrations de sons. Sans être des descriptions de tableaux réels, ces passages sont parfois inspirés d’une oeuvre ou d’un peintre en particulier, et trahissent parfois une tentative, de la part des romanciers, de rivaliser avec les artistes qu’ils admirent. Si leur situation géographique varie, ces scènes partagent un ton mixte qui combine la gaîté et la mélancolie. La Seine et ses bords fonctionnent souvent chez les Goncourt comme un locus amoenus trompeur ou révélateur : s’y déroulent de brefs interludes idylliques et amoureux qui annoncent néanmoins des naufrages imminents. Un ton mélancolique teinte aussi maintes mentions de la Seine dans le Journal, surtout sous la plume d’Edmond veuf de son frère.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".