Les personnes âgées de demain : que peut nous dire l’analyse générationnelle ?
Bibliographic record
Abstract
In France and Québec, the older population is being replaced rapidly, though this will tend to slow down in the coming decades. The socioeconomic characteristics of tomorrow’s older population are partly known through the characteristics of younger generations who will be the older persons of tomorrow. By way of example, we selected certain characteristics that will influence the income of tomorrow’s older population: participation in the labour market, income level during working life, financial preparation for retirement, and status related to residential property. The male participation rates at the end of their working life are higher in Québec than in France. An important factor in the increase of income from one generation to the next is women’s income, which will likely continue to grow. Very positive effects on their retirement-specific income. With a property acquisition profile that is very different in both Québec and France, the rate of ownership has increased from one generation to the next. The income of family units is also increasing from generation to generation. Several data presented here indicate that the older population of tomorrow will have greater autonomy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".