Les déterminants des crises financières récentes des pays émergents
Bibliographic record
Abstract
L’objet de cet article est de montrer empiriquement la supériorité d’une explication des crises financières récentes dans les pays émergents par la combinaison de facteurs endogènes et exogènes aux pays affectés plutôt que par le seul jeu de l’une ou l’autre de ces deux catégories de facteurs. À cet effet, nous bâtissons notre démarche sur des estimations d’un modèle de panel à erreurs composées ainsi que sur des statistiques de test des modèles emboîtés (test de Fisher). À ce jour, des éléments de preuve de la supériorité de cette approche n’ont pu être apportés que dans le contexte particulier de telle ou telle crise. Notre contribution fournit cette preuve à partir des données portant sur 14 pays émergents et 3 épisodes de récentes crises (mexicaine 1994, asiatique 1997 et russe 1998), couvrant ainsi la plupart des crises financières de ces 10 dernières années.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".