Bibliographic record
Abstract
En Asie-Pacifique, la Banque asiatique de développement (bad) octroie chaque année entre 5 et 6 milliards de dollars de prêts. Cela lui permet d’exercer une influence considérable sur les orientations des pays en développement de la région. La bad est aujourd’hui le troisième plus important donateur en Asie du Sud-Est, après le Japon et la Banque mondiale. Son implication dans le processus d’intégration régionale est avérée dans le plan de développement de la péninsule indochinoise, plus connu sous son appellation anglaise de Greater Mekong Subregion (gms). Non seulement ce projet ambitieux est considéré comme le plus grand projet transnational de la planète, avec cinq pays de l’Asie du Sud-Est continentale et deux provinces chinoises, mais encore il est devenu le prototype de la grande vision de développement régional que la bad cherche à tester pour le dupliquer dans d’autres zones. C’est dans ce contexte que le mode opératoire de la bad sera également analysé au regard des critiques dont elle fait l’objet.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".