Bibliographic record
Abstract
Many western nations have experienced a rise in the number of marginalised and deprived inner-city neighbourhoods. Despite a plethora of research focused on these areas, there remain few studies that have sought to capture the ‘optimality’ of ageing in place in such places. In particular, little is known about why some older people desire to age in place despite multiple risks in their neighbourhood and why others reject ageing in place. Given the growth in both the ageing of the population and policy interest in the cohesion and sustainability of neighbourhoods there is an urgent need to better understand the experience of ageing in marginalised locations. This book aims to address the shortfall in knowledge regarding older people’s attachment to deprived neighbourhoods and in so doing progress what critics have referred to as the languishing state of environmental gerontology. The author examines new cross-national research with older people in deprived urban neighbourhoods and suggests a rethinking and refocusing of the older person’s relationship with place. Impact on policy and future research are also discussed. This book will be relevant to academics, students, architects, city planners and policy makers with an interest in environmental gerontology, social exclusion, urban sustainability and design of the built environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.009 |
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".