Bibliographic record
Abstract
Our Canadian retirement system contains three pillars, focusing on providing all members of Canadian society with a minimum and guaranteed standard of living for retirement, throughdirect transfers to help the working poor (first pillar) to Canada Pension Plan payments through mandatory monthly deductions (second pillar) funding current retirees and through taxdeductions offered via Canadians’ private savings (third pillar). Yet, with the baby boom generation presently retiring, and the current workforce positively shrinking, how will our monthly paycheck deductions towards Canada Public Pension payments be affected? Did you know that when you are born in 1985, you receive approximately $0.70 in retirement back from the government for every $1 contribution made from your current paycheck, and $0.60 back for every $1 contribution if born in 1995? These demographic changes are negatively affecting the future returns of our Canada Public Pension payments, despite a +$150 billion Canada Pension Plan Investment fund and the other two pillars of the Canadianretirement system. Either one improves the second pillar as it is, affecting the other two pillars, or one completely replaces that system with alternative options. This presentation will focus on the alternative options available and what can be done to mitigate the negative effects of the present demographic changes affecting the status of our Canada Public Pensions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".