Gerontological Perspectives on Crime and Nuisance
Bibliographic record
Abstract
This paper investigates the perceptions of the elderly in relation to crime and nuisance and the fear of crime associated with stereotypical British housing designs. Demographically, this diverse though highly urbanized group continues to grow; group members' observations, therefore, have increasing social relevance and political importance and are crucial for assessing and informing both current policy and the evolution of future policy initiatives. Crime Prevention Through Environmental Design (CPTED) has become popular once again in America, Australia, Canada, South Africa, as well as in Europe and Britain. A crucial dimension to this theory concerns the perception of "territoriality," "surveillance," and "image" within the design of the built environment derived from Newman's "Defensible Space" concepts (1973). This paper presents and discusses the ways in which the elderly associate crime and nuisance with a range of traditional housing designs. The findings strongly reinforce Newman's theory. The paper concludes that the design and, perhaps more importantly, the management of residential housing influence the perceived levels of crime, nuisance, and fear of crime, and the "defensible" qualities of each specific design. Such perceptions will arguably affect elderly people's ability to maintain their privacy, dignity, and autonomy, their physical and psychological well-being, and their social inclusion. Policy implications for housing the elderly safely within the community are reviewed.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".