Population Ageing and Care of the Elderly: What Are the Lessons of Asia for Sub-Saharan Africa?
Bibliographic record
Abstract
The population age structures of Sub-Saharan African (SSA) countries are among the youngest in the world. It is estimated that by the year 2000, 45 per cent of the SSA population would be below the age of 15, as compared with 33 per cent in South-East Asia, which has started its demographic transition much earlier. But fertility has started to decline in several SSA countries. Kenya, Rwanda, Zimbabwe, Botswana, South Africa and Côte d’Ivoire have experienced moderate to large declines in fertility with smaller declines occurring in Malawi, Tanzania, Zambia, Cameroon, Central African Republic, Burkina Faso, Gambia, Ghana, Mauritania, Senegal and Sierra Leone (Cohen, 1998). This trend is going to continue and will be repeated in other SSA countries, whose population by the middle of the twenty-first century will stabilise at replacement level. Other developing countries in Asia and Latin America have started their demographic transition earlier, and their population, particularly in South and South-East Asia, will reach replacement level by the first quarter of the twenty-first century (see Table 6.1). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".