Enquêtes au pays de Lupin : fictions romanesques policières en milieu patrimonial (musées, collections, monuments, paysages)
Bibliographic record
Abstract
L'article porte sur les récits d’enquêtes policières qui voient le jour dans le milieu des musées, des collections et des galeries d'art. Il postule la convergence d’intérêt, depuis le XIXe siècle, pour les notions épistémologiques d’« indice » et de « détail », laquelle constitue l’un des ferments de l’histoire de l’art. L'analyse étudie quelques jalons exploratoires du sujet à partir d’un corpus circonscrit à des récits d’enquête et à la figure, emblématique d'Arsène Lupin (Maurice Leblanc, Michel Bussi), relevant d’un genre littéraire policier, hybride par nature, à la croisée de plusieurs dénominations, distinctes mais convergentes : « chasse au trésor » ou bien encore « polar ». Que nous dit cette rencontre entre intrigue à valeur d’enquête, histoire et Histoire ? Comment penser la fiction et donc la recherche de vérité au musée ?
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".