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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".