Bibliographic record
Abstract
Old age is difficult to define, so many terms overlap or clash with each other, all of which raise a number of issues: the elderly, the third age, the fourth age, senior citizens, pensioners, etc. It is not easy to determine the threshold for entering the period of life commonly known as old age. One thing is certain, old age has changed profoundly. From now on, it has become for everyone, albeit with profound inequalities, a normal stage of life. Social security systems combined with the considerable progress in medicine have made it possible to increase the length of retirement. Whereas in 1950 a man retiring at 65 could expect to live for about 12 years, today life expectancy at 60 is over 20 years for men and over 25 years for women. However, this simple observation has much more complex consequences in terms of the social identity, integration and social behaviour of these new population groups. Despite an ageing population, Switzerland has a total labour force of 4.706 million people. In the fourth quarter of 2019, the participation rate of the population aged 15 and over was 68.1%. This puts the country in second place in Europe behind Iceland (79.9%). Switzerland's neighbouring states have significantly lower levels (Germany: 62.6%, Austria: 61.4%; France: 55.5%, Italy: 49.9%). In particular, Switzerland has one of the highest rates of employed women in Europe. The percentage of employed women increased significantly between 2010 and 2019, from 56.9% to 60%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".