The 2012 Neighbourhood Watch Australasia Survey: Methodology and Preliminary Findings
Bibliographic record
Abstract
One of the main explanations for high levels of crime, social disorder, and fear of crime in certain neighbourhoods has been the erosion of informal social control processes that are believed responsible for maintaining order (Rosenbaum, 1987). Similarly, over the past several decades discussions have been had about a declining sense of community and social cohesion within neighbourhoods. Community crime prevention programs such as Neighbourhood Watch have been recommended as a feasible and attractive solution to these crime-related neighbourhood conditions. Neighbourhood Watch originally grew out of a movement in the 1960s in the United States that involved greater involvement of citizens in the prevention of crime in their neighbourhoods. Since the 1980s the number of Neighbourhood Watch schemes has spread to other nations, including Australia, New Zealand, the UK and Canada. While the names of these organisations can vary by jurisdiction (e.g, Neighbourhood Watch; Neighbourhood Support; Block Watch, etc), their main objectives do not. Common to all is the emphasis on community crime prevention where citizens work together and with the police to reduce crime in a community. Neighbourhood Watch programs aim to reduce crime by having citizens watch out for and report suspicious activities to the police and to deter potential criminals from offending. These tasks are usually achieved by improving citizens’ awareness of public safety, by reducing vulnerability to crime through helping citizens to install security devices, and by improving attitudes and behaviours toward reporting crime and suspicious behaviour to police. One particularly important aspect of Neighbourhood Watch programs has been to bring about social interaction between residents of a community and to maintain a degree of familiarity with neighbours so that the detection of strangers in the community can be easily achieved.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".