Vulnérabilité du secteur des entreprises et activité agrégée
Bibliographic record
Abstract
Il ressort de cet article, qui utilise des microdonnees relatives au secteur des entreprises, que les societes non financieres etablies au Japon et dans les grands pays europeens en 2003 etaient plus vulnerables a une hausse des taux d’interet a court terme qu’elles ne l’etaient en 1993, lorsque a debute la precedente phase de resserrement monetaire (sachant qu’une entreprise est consideree comme vulnerable si elle affiche un ratio dettes/fonds propres eleve et une faible capacite a assurer le service de sa dette). Aux Etats-Unis et au Canada, par contre, les entreprises semblent mieux preparees a faire face aux hausses de taux d’interet. En outre, en examinant uniquement les donnees de 2003, les auteurs parviennent a la conclusion que les entreprises du Japon et des grands pays de la zone euro sont plus vulnerables que celles des Etats-Unis, du Canada et du Royaume-Uni. Ces microdonnees sont egalement utilisees afin d’elaborer pour chaque pays une mesure de vulnerabilite de l’ensemble de l’economie, qui se revele correlee de maniere significative aux variations futures de la croissance du PIB et de l’investissement.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".