Ageing and development in Argentina
Bibliographic record
Abstract
Introduction Argentina is a relatively wealthy country, with an embracing welfare system and an aged population structure. As such, it may offer insights relevant to the future for other developing countries. Despite this, it is widely accepted that Argentina's economic performance over the past 70 years has been poor and erratic. The causes of this decline are complex, but this chapter will seek to assess whether population ageing has played any role in hindering growth. The chapter will also consider how this gradual decline has affected experiences of ageing and the lives of older people. It follows broadly the same structure as the other country case studies in this book. First, it provides a brief overview of Argentina's economic and social development. This is followed by an analysis of demographic trends, including fertility transition and population ageing. The chapter then focuses on a number of key themes. In this case, they cover potential economic impacts of population ageing, as well as the provision of social security and health services, and the care economy. These more general analyses are complemented by three life histories of older Argentines, which reveal the diversity of old-age experiences and the different ways in which national factors intersect with personal biographies. Social and economic development in Argentina In the first half of the 20th century, Argentina was one of the richest countries in the world. In terms of development and social welfare, it was seen as much closer to Australia, Canada and New Zealand than to its Latin American neighbours (Platt and Di Tella, 1985). By the end of the 20th century, decades of extreme economic instability and growing social inequality had relegated Argentina to ‘developing country’ status. The scale of Argentina's relative decline was dramatic: in 1950, average per capita incomes were still 84% of the Organisation for Economic Co-operation and Development’s, by 1987 they had fallen to only 43% (Della Paolera and Taylor, 2003). The Argentine experience demonstrates that development should not be understood as an inevitably linear and irreversible process. Current cohorts of older people were born into a prosperous country and lived through its decline, which will have shaped the courses of their lives and their situation in old age.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".