The state in ageing Canada: from old-age policies to lifecourse policies
Bibliographic record
Abstract
Introduction Population ageing directs the attention of policy makers to older people. Policy makers increasingly have to ponder what today's older people need and how existing institutions can adapt in order to accommodate an ageing population. However, when doing so, policy makers cannot focus on older age alone. After all, experiences during one's youth and middle age can have a profound impact on one's situation in old age. To address population ageing effectively, policy makers would therefore need to account for long-term effects on old age. Long-term effects that unfold over a person's life are called ‘life-course effects’ (Grenier, 2012). While considering lifecourse effects in policy-making sounds like a small step, it in fact has major implications. Traditional old-age policies treat older people as a separate and distinct population group, whereas policies embracing the lifecourse perspective see old age as a stage that (almost) everybody reaches at some point in time. Consequently, old-age policies explicitly address older people, while lifecourse policies take a much broader approach (Anxo et al, 2010). On the one hand, they strive to shape the situation in old age by influencing people at earlier ages. On the other hand, they strive to address the phenomenon of population ageing by reassessing the needs and potentials of all age groups. Thus, lifecourse policies address the entire population over longer periods of time. This chapter explores how lifecourse effects can be incorporated into policies for old age. It explains what this shift in perspective entails and why it is advantageous, and it gives examples of policies that reflect this idea. To reach these goals, this chapter proceeds in five steps. First, it presents theoretical reflections on the character and specificities of lifecourse policies. Then it compares lifecourse policies with old-age policies. Subsequently, this chapter discusses stumbling blocks on the road towards introducing lifecourse policies. Then, it presents examples of lifecourse policies from Canada, where policy makers are currently implementing lifecourse approaches in a number of new programmes. Finally, this chapter discusses advantages and limitations of lifecourse policies. Understanding lifecourse policies Lifecourse policies follow individuals from the cradle to the grave, supporting and stimulating them whenever needed. By intervening, these policies strive not only to solve current problems, but also to prevent hardship at later ages.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".