Bibliographic record
Abstract
The income security of retired Canadians has become an increasing challenge for Canadian policy makers. There has been a significant decline in the coverage of workplace pensions in the private sector and a dramatic shift in the structuring of private-sector pensions away from defined-benefit plans and towards defined contribution plans over the past 20 years. Meanwhile, life expectancies are continuing to rise, which implies that pensions and retirement income need to last longer. Retirement income programs are also facing the retirement of 8 million baby boomers leaving the labour force over the next 15 years (out of a labour force of 18.7 million in 2007). These factors can be expected to place severe financial pressures on retirement income schemes, with implications for the economic well-being of oncoming retirees. The Guaranteed Income Supplement (GIS) benefit is targeted to help seniors with the intention of raising the incomes of those without sufficient resources of their own or from other government sources to an acceptable level. A CLSRN study by Ross Finnie (University of Ottawa), David Gray (University of Ottawa) and Yan Zhang (Statistics Canada) entitled “The Receipt of Guaranteed Income Supplement (GIS) Status Among Canadian Seniors – Incidence and Dynamics†(CLSRN Working Paper no. 115) measures the incidence of receipt of GIS payment among those over 65 in Canada and the dynamics of entries and exits from this state. Income in retirement – whether from public or private pensions, savings or employment – is an important determinant of the quality of life for Canadians leaving the workforce. A CLSRN paper entitled “How do the level and composition of income change after retirement? Evidence from the LAD†(CLSRN Working Paper no. 114), by Ross Finnie (University of Ottawa) and Byron G. Spencer (McMaster University), uses a unique dataset that allows the authors to follow individuals from their prime working years into retirement. They find that while incomes drop sharply at retirement, the longer term rates of income replacement are relatively stable over the retirement period.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.647 | 0.467 |
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".