Bibliographic record
Abstract
Previous research has consistently shown that repeat crime victimization is common.More recently, research has shown that near repeat victimization is also common, whereby targets located in close proximity to previously victimized dwellings/people/vehicles (depending on the crime) are at an increased risk of also being victimized.However, this elevated risk is only temporary and subsides over time.This near repeat space-time clustering has been found across various crime types (e.g., burglary, theft from motor vehicle (TFMV), gun crime, etc.) as well as across jurisdictions.However, the precise space-time patterning of crimes is location-specific.To date, no published research exists that has examined near repeat victimization using Canadian data; the current study fills this gap.This dissertation consisted of 4 phases of analyses.Phase 1 determined the exact space-time clustering of three crime types (burglary, TFMV, common assault) across three Canadian cities (Edmonton, AB, Moose Jaw, MB, Saint John, NB).Phase 1 results found significant near repeat space-time clustering for Edmonton burglary, Edmonton TFMV, and Saint John TFMV, with the exact near repeat space-time pattern varying from one data file to the next.Phase 2 analyses used the time and distance over which crime clusters (as found in Phase 1) to generate prospective risk surfaces.Risk surfaces were also generated using two traditional hot-spotting methods.Overall, the various hotspot mapping techniques examined were found to be comparable in their accuracy at predicting future crime.Phase 3 examined whether it was possible to improve the accuracy of prospective hot-spotting by considering three different strategies.Although Phase 3 results suggested that the three strategies examined were not effective at improving predictive accuracy of the SPACE-TIME CLUSTERING OF CANADIAN CRIME iii maps, some interesting trends did emerge, which may have practical implications.Finally, Phase 4 investigated whether near repeat burglaries in one Canadian city (Edmonton, AB) were more likely to be committed by the same offender than more distant burglaries.Phase 4 results suggested that serial offending by the same offender offers a viable explanation for near repeat crime.The theoretical and practical implications of these results, as well as some limitations and directions for future research, are also discussed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".