Bibliographic record
Abstract
De nombreux pays (Irlande, Nouvelle-Zélande, Pays-Bas, Espagne, Portugal, Norvège) ont récemment créé des fonds de réserves pour les retraites, pour préfinancer une fraction des engagements de leurs régimes publics de retraite par répartition. D'autres pays (Suède, la Finlande, le Japon, Canada, États-Unis), ayant depuis longtemps accumulé des réserves, ont modifié les règles de gestion financière afin d'améliorer l'allocation des investissements et d'accroître le rendement financier. L'accumulation de réserves dans les régimes par répartition et l'optimisation de leur gestion financière sont devenues des éléments prépondérants dans le processus de réforme des systèmes de retraite. Le panorama de ces expériences montre une grande diversité des pratiques nationales quant aux objectifs assignés aux fonds de réserves, au montant et à la nature des ressources financières affectées aux fonds, aux modalités de la gestion financière. Classification JEL : C23, H55, J26.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 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; 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".