Neighbourhood characteristics and the distribution of crime in Winnipeg
Bibliographic record
Abstract
This research paper explores the spatial distribution of crime and various social, economic and physical neighbourhood characteristics in the City of Winnipeg. Analysis is based on police-reported crime data from the 2001 Incident-based Uniform Crime Reporting Survey (UCR2), the 2001 Census of Population, and City of Winnipeg land-use data.\n\nIn general, results support previous research suggesting that crime is not randomly distributed within cities, but is associated with the distribution of other factors related to the population and land-uses of the city. In particular, crime in Winnipeg in 2001 was concentrated in the city centre, representing a relatively small proportion of the total geographic area of the city.\n\nResults point to significant differences in the characteristics of high- and low-crime neighbourhoods. For instance, high-crime neighbourhoods were characterized by reduced access to socio-economic resources, decreased residential stability, increased population density and land-use patterns that may increase opportunity for crime.\n\nAfter taking into account all other factors, the level of socio-economic disadvantage of the residential population in a neighbourhood was most strongly associated with the highest neighbourhood rates of both violent and property crime.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".