Bibliographic record
Abstract
La modélisation financière moderne instrumente massivement des lois d'échelle pour la formalisation des fluctuations des marchés, à travers l'usage des processus aléatoires dans les équations de comportement des cours boursiers. D'abord implicites, présentes dans le mouvement brownien mais non perçues en tant que telles, les lois d'échelle sont réapparues explicitement comme enjeu de la modélisation depuis que les autorités de tutelle des marchés financiers ont attiré l'attention des établissements bancaires sur le problème du contrôle des risques, mal quantifiés par les distributions gaussiennes classiques. On présente les éléments de ce débat, en adoptant comme fil conducteur la postérité des modèles fractals de Mandelbrot dans le sillage desquels s'inscrivent les principaux conflits de modélisation des variations boursières depuis les quarante dernières années. On montre que la question de l'existence de lois d'échelle renvoie à la question plus fondamentale de la nature du temps des marchés.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".