Policy implications of population ageing in West Africa
Bibliographic record
Abstract
Purpose The purpose of this paper is to discuss trends in demographic ageing in West Africa and asks the question of what policy challenges are posed by the increasing presence of older persons in the subregion. We explore the unique dimensions of population ageing in the subregion, including its rural‐urban and gendered distributions, the occupational history of older persons, among others with the view to identifying the health, housing, and income security implications of ageing. The paper discovers and reviews what policy initiatives are being pursued in respect of older persons and suggests ways for their improvement. Design/methodology/approach The paper draws on the existing literature on ageing and policy in both published and grey sources, including national and international policy documents. The discussion looks at policy responses in Ghana as a case example for the West African context. Policy information pertaining to Ghana is interpreted in light of the first author's personal familiarity with the context as a national of that country. The age of adults in this context is hard to determine due to low birth registration. In this paper older persons are defined as those 60 plus in chronological years, the age of retirement in Ghana. Findings It is established that older persons are concentrated in the rural areas of West Africa and a higher proportion of this demographic group is female. Further, the majority of older persons in West Africa has low formal literacy, is in the informal economy, and has no income security in old age. Yet, older persons continue to play the significant role of grandparenting. This examination of Ghana's policy on ageing revealed inadequacies which need to be addressed. A key recommendation is a policy of universal non‐means‐tested old age security to provide basic income for persons aged 60 years and above. Originality/value A recommended policy of universal non‐means‐tested old age security to provide basic income for persons aged 60 years and above in Ghana is the original contribution of this paper.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".