L’économie de partage (ou de l’utilisation partagée) et l’aggravation du risque en assurance
Bibliographic record
Abstract
La forte croissance des transactions dites de partage ces dernières années laisse croire à un bouleversement de notre façon de consommer. Les plateformes en ligne facilitant le partage, le prêt ou le louage entre individus incite à prendre (ou reprendre) conscience qu’il n’est pas nécessaire de posséder un bien pour profiter de son utilité. Quant aux biens que l’on possède déjà, les plateformes d’économie collaborative permettent d’en tirer un revenu supplémentaire avec peu d’effort et à l’intérieur de structures qui font oublier les risques encourus. Du point de vue d’un assureur, ces changements ont un impact non négligeable sur le risque des biens et des individus assurés. Dans cet article, nous offrons un bref survol de la littérature sur le sujet et présentons quelques conclusions quant à l’aggravation du risque en lien avec les plateformes telles Uber et Airbnb.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".