L’arbre de la Raison – La fabrique d’un motif pictural au début du XIXe siècle
Bibliographic record
Abstract
The nineteenth century witnessed the rise of a specialized body of writing that ascribed a central place to the tree as object of knowledge and that contributed to the development of a pictorial motif that I call the “tree of Reason” (“l’arbre de la Raison”). Appearing in Europe in the early nineteenth century, the first “landscape lessons” aimed at beginner artists and amateurs rested on a new pedagogy that promised the reader and student quick results, regardless of their artistic talent. Their approach was based on an extreme form of rationalisation and simplification, which followed from the theoretical and aesthetic principles of Neoclassicism. Behind the word “landscape” (paysage) found in the titles of these successful manuals lay the motif of the tree, which constituted their main subject. This article begins with an examination of Principes raisonnés du paysage, published in 1804 by Nicolas-Alphonse Michel Mandevare, and analyzes the drawing method that it proposes. Regarded as the result of a construction, of an arrangement of its different parts, this “tree of Reason” highlights the end of a big artistic cycle, which was paradoxically to have little influence on the tenets of modern landscape that took hold in the 1830s. This rationalistic and reasoned practice thus ended in an impasse, in which both tree and landscape became petrified in a set of codified rules and references.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".