Bibliographic record
Abstract
• Le present article decrit les caracteristiques fondamentales du modele ainsi que ses principales donnees d’entree et de sortie. titre d’agent financier du gouvernement, la Banque du Canada fournit des analyses et des conseils ayant trait a la gestion du portefeuille de la dette publique interieure. Les decisions en matiere de gestion de la dette doivent s’appuyer sur des hypotheses concernant les taux d’interet futurs, la tenue de l’economie et la politique budgetaire; or, lorsqu’une strategie de financement est arretee, aucun de ces facteurs n’est connu avec certitude. En outre, le gouvernement dispose de plusieurs options de financement (bons du Tresor, obligations nominales et obligations indexees sur l’inflation) pour respecter son objectif de reduire au maximum les charges d’interet tout en adoptant un profil de risque prudent et en favorisant le bon fonctionnement des marches des titres d’Etat. Le personnel de la Banque a par consequent elabore un modele mathematique pour soutenir le processus decisionnel. Le present article expose les aspects cles du probleme que doit resoudre le gestionnaire de la dette ainsi que les principales hypotheses a la base du modele de gestion de la dette; certains resultats issus du modele sont en outre presentes a titre illustratif.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".