Bibliographic record
Abstract
Abstract With a policy mix that comprises of several major social programs, Canada’s pension system is among the best in the world in reducing poverty in old age and providing a high replacement rate for low-income retirees. In this chapter, we focus on the interaction between two closely related components of Canada’s pension system as a policy mix particularly successful at reducing old-age poverty: the Guaranteed Income Supplement (GIS) and the Old Age Security (OAS) program. More specifically, we analyse the primary root of this policy success: the ‘failure’ to dismantle a program that was meant to be temporary, the Guaranteed Income Supplement (GIS); and its complementarity to the Old Age Security (OAS) program. The GIS was introduced in 1967, as a transitory measure to tackle expediently the prevalent poverty amongst Canadian seniors while expected to disappear with the maturation of the Canada Pension Plan/Québec Pension Plan. Not only was this program never abolished, it failed to generate the kind of stigma associated with social assistance benefits by virtue of linking the program with the quasi-universal OAS program. As a result of this policy design, it is an income-test program—not to be confused with a means-tested program where assets are also taken into consideration—where older adults feel they’reentitled OAS beneficiaries. The combination of OAS and GIS provides a relatively generous floor for retirees with limited resources. This points to the close and complementary relationship of key elements of the public pension policy mix in Canada.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".