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. In 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. Results 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. After 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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".