Bibliographic record
Abstract
Derrière un seul et même mot – l’« esthétisation » – se cachent des constats que tout ou presque sépare. Peu étonnant que les différents débats autour de cette notion, en France, en Allemagne ou aux États-Unis, aient souvent engendré des incompréhensions mutuelles. Avant même de savoir si l’esthétisation du monde est un phénomène désirable ou condamnable, il faut éclairer les arrière-plans conceptuels qui orientent ce diagnostic, et qui lui confèrent un sens parfois diamétralement opposé. En ouverture du dossier, l’article entreprend de faire la généalogie de ces débats, avec trois moments-clés – le moment 1800, l’entre-deux-guerres (1919-1939), et enfin les années post -68 (1970-1990). Il propose ensuite une typologie pour s’orienter dans ces débats et comparer les quatre versions de ce qu’« esthétisation » peut vouloir dire.
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 | 0.004 |
| 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 | 0.018 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".