Breaking Down Walls: New Solutions for More Effective Urban Crime Prevention in South African Cities
Bibliographic record
Abstract
In the late apartheid period South African suburbs began to change dramatically in both their appearance and design. Essentially, housing was designed with the aim of keeping intruders out. This included constructing increasingly high walls, implementing electrified fences and laser beams. Alongside these ‘investments’ and design innovations came the massive growth of the private security industry. A new mentality emerged which focused on the fortification of home and office space. Initially, this was strongly supported and bolstered by the private security industry that had vested interests in the rush to monitor space and strengthen security. Whether or not high walls and electrified fences do indeed reduce experiences of crime victimisation for individual home owners and residents is debatable. The private security industry and the police now suggest that walls might not provide the security home owners believe they do. This research investigates whether walls, electric fences and beams, among other tools, succeed in reducing fear of crime and victimisation, from the perspective of those who police, i.e., public and private security organisations. The aim is to establish whether policing agents view walls as an aid or hindrance to policing and security management. The ‘praxis’ goal of this research is, through public engagement, to shift paradigms about walls and security and to bring to the fore the importance of natural surveillance and neighbourly contact in making urban spaces safer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".