Bibliographic record
Abstract
The OECD has undertaken a comparison of the resources of older people in nine OECD countries – Canada, Finland, Germany, Italy, Japan, the Netherlands, Sweden, the United Kingdom and the United States – and has examined how the incomes of older people are influenced, directly and indirectly, by government policies. The study finds that a new policy agenda is emerging, one that is likely to focus on how policy can support: A transition from full-time work to retirement that is later, on average, than at present and that provides greater opportunities for more flexible and gradual pathways to full retirement. There would be a realistic possibility for many people to have a continued attachment to the labour force, and an active life in society, well into later life. However, highest priority would be on encouraging later retirement for those who now retire well before age 65. A diversified system of income support for older people during retirement (and the transition to ... L’OCDE a entrepris de comparer les ressources des personnes âgees dans neuf pays de l’OCDE - - Allemagne, Canada, Etats-Unis, Finlande, Italie, Japon, Pays-Bas, Royaume-Uni et Suede --, cherchant, en particulier, a voir comment les revenus des personnes âgees subissent, directement ou indirectement, l’incidence des politiques publiques2. Cette etude met en evidence l’apparition d’un nouveau defi pour les pouvoirs publics, qui devront s’efforcer d’accompagner certaines evolutions : Faire que le passage d’une activite a plein temps a la retraite s’effectue plus tardivement, en moyenne, que ce n’est le cas actuellement, et selon des modalites plus souples et plus graduelles avant d’en arriver a une cessation complete d’activite. Ainsi, de nombreuses personnes pourraient continuer de garder un lien avec le monde du travail et continuer de mener une vie active au sein de la societe jusqu’a un âge avance. Cependant, la premiere priorite serait d’encourager les personnes ...
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.002 |
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".