Refexive Planning for Later Life: A Conceptual Model and Evidence from Canada
Bibliographic record
Abstract
In this paper, we present a conceptual model to describe an individual's preparations for later life. Situated in the life course perspective, this model invites a comprehensive and systematic study of later life planning. It describes a dynamic process that portrays the interplay between social structure and human agency. Through its consideration of collective preparations (the public protection programs offered by the state), individual preparations (financial and non-financial), and the interplay between them, this model provides fresh insight into the existing literature on retirement planning, the timing of retirement, savings, and consumption patterns in later life. Moreover, the model may be used to structure research questions, to guide policy decision making and to point the direction for the design and content of future research studies. While the purpose of this paper is primarily the development of a conceptual model, we illustrate the model using the results of a self-completion semi-structured questionnaire on this topic that was completed by a convenience sample of 240 seniors in Canada. We conclude by suggesting a number of research questions that may be generated from the model.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".