Sources de revenu de retraite au Québec 2004 - 2030: une analyse de microsimulation
Bibliographic record
Abstract
Ce rapport prA©sente une premiA¨re version du ModA¨le de Micro-Simulation de l'UniversitA© Laval (SimUL) visant A simuler l'A©volution de divers indicateurs reliA©s aux changements dA©mographiques et aux revenus de retraite pour la pA©riode 2004-2030 au QuA©bec. SimUL prA©voit que la croissance importante dans le niveau d'A©ducation des individus dans le temps influencera significativement les revenus futurs de retraites. Les revenus de pensions privA©s ainsi que les prestations de la RA©gie de rentes du QuA©bec (RRQ) des individus de 65 ans et plus augmenteront d'environ 1 A 3% par annA©e en termes rA©els de 2010 A 2030. Le modA¨le prA©voit A©galement que la proportion des femmes admissibles aux prestations de RRQ passera d'environ 80 A 99% de 2004 A 2030. Les nouveaux retraitA©s seront ainsi plus riches que les retraitA©s actuels, ce qui aura comme effet de diminuer considA©rablement la proportion des individus admissibles aux prestations de SupplA©ment de revenu garanti (SRG), qui passera de 39,8% en 2010 A 24,4% en 2030. Les dA©penses liA©es aux rA©gimes publics de retraite augmenteront fortement de 2010 A 2030. Le coA»t total des prestations de SA©curitA© de la vieillesse pour le QuA©bec passera de 6 milliards A 11 milliards, celui des prestations de RRQ passera de 8 milliards A 18 milliards, mais celui des prestations de SRG se stabilisera aux alentours de 1,6 milliards. SimUL prA©voit finalement que le coA»t, toujours pour le QuA©bec seulement, de la prestation complA©mentaire de SRG annoncA©e par le gouvernement du Canada dans son plan d'action A©conomique de 2011 demeurera stable A environ 15 millions de dollars.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".