Bibliographic record
Abstract
Où est la Seine dans les Tableaux parisiens ? Cette question toute simple à résoudre en apparence recèle maints pièges qu’il est nécessaire d’examiner si l’on espère offrir un portrait juste et éclairé de l’image du fleuve parisien dans la plus référentielle (et la plus commentée) des sections des Fleurs du Mal. Parmi ces pièges, citons : les liens entre réel et idéal chez Baudelaire, les stratifications vertigineuses du sens dans la poésie baudelairienne, les faux-semblants et les effets spéculaires souvent trompeurs, l’exotisme, le romantisme, le symbolisme… de même que l’abondance de la critique ancienne et récente sur toutes ces questions. La présente étude aborde ces problèmes au fil d’une enquête qui aboutit à un paradoxe tout baudelairien : plus la présence de la Seine se fait ténue au sein de ces Tableaux parisiens, plus elle est significative.
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 | 0.003 |
| 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 | 0.026 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".