Bibliographic record
Abstract
Introduction The previous chapter reviewed the last 40 years of empirical literature related to our understanding of the relationship between the older person and their environment. While this yielded a breadth of knowledge, there remain some substantial shortfalls within empirical knowledge that require urgent focus, particularly set against a context of other trends, in particular population ageing and urbanisation. The focus of this chapter is on examining ageing in urban environments and what this means for the person–environmental fit. The first section of the chapter briefly examines trends in both population ageing and urbanisation. The next section discusses factors present in urban spaces that might support and hinder ageing, and what is currently known about older people ageing in urban centres. Critically, the chapter raises the question of the current ‘optimality’ of urban neighbourhoods to support the health and well-being of those ageing in urban centres. Trends in urban ageing Population ageing and urbanization are two global trends that together comprise major forces shaping the 21st century. (WHO, 2007, p 6) Trends in population ageing and urbanisation make the understanding of urban ageing highly relevant to the agenda on sustainable development. Urban development has been described as ‘one of the most powerful of the forces which are shaping the geography of the contemporary world’ (Clark, 2000, p 15); transforming the lifestyles of almost half of the world's population. A recent report by the United Nations Population Fund (UNFPA, 2007) predicted that by 2008, for the first time in history, more than half of the world's population – 3.3 billion people – would live in urban areas, and by 2030 this figure is expected to be almost five billion (see Figure 3.1). In Europe, almost 75% of the population already live in urban areas (for example, 80% in the UK, 77% in France and Spain, 73% in Germany and 68% in Italy). Canada and the US have similar percentages of the population living in urban areas, 81% and 79% (2008 figures). There has also been a rise in megacities – cities with a population of 10 million or more – and this is expected to rise further in the coming years. The United Nations predicts that by 2015, 23 cities will be defined as megacities, of which most will be in the developing world (UNFPA, 2007); and by 2030, three out of every five people will live in urban areas (WHO, 2007).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.150 | 0.038 |
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".