Aptitudes fonctionnelles, environnement et données probantes pour vieillir en bonne santé
Bibliographic record
Abstract
The African continent and its territorial heterogeneity are characterised and illustrated by a complexity and diversity of demographic and economic contexts, including differentiated stages in the process of demographic transition, uneven economic performance, and accelerated and multiform urbanisation. Population growth in Africa is set to explode in the coming decades and the continent will also be faced with a phenomenon it has not previously encountered: a substantial and rapid increase in the number of older persons. Yet for now, national policies more frequently prioritise youth than people in their old age. Owing to considerable socio-economic disparities between urban and rural areas, as well as growing urbanisation and the resulting issues in housing, poverty and changes in inter-generational relationships, ageing is experienced in a number of contrasting ways on the continent. The use of several levels of analysis highlight the way in which territorial disparities influence the variability of the ageing process in Africa, and the recurrence of certain situations raising numerous questions. Reflecting the territorial heterogeneity of Senegal, two case studies illustrate the highly uneven spatial distribution of healthcare services and the role they play in accentuating the vulnerability of older persons.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".