Back to school days: Crime seasonality in a campus-dominated community
Bibliographic record
Abstract
Crime on university and college campuses is an ongoing concern for students, faculty, administrators, and policy makers ( Fisher & Sloan III, 2022 ). However, much of this research focuses on university and college campuses that are separated from the rest of the city or community they are located within. Doing so is important, given that integrated campuses create particular crime opportunity structures that can impact members of the university and the community more broadly. In this study, we examine crime trends in Brantford, Ontario, where the university is fully integrated into the downtown. We ask will the influx of a large population of students and staff during the school year influence the expected patterns of crime in this area as compared to the rest of the city? We find that assault increases significantly in the university campus area at the beginning of the school year, but returns to expected patterns soon after. This is important when considering safety planning for campus communities, particularly at the start of the school year. Findings indicate that the typical patterns of seasonality can be impacted by a large shift in population, and this should be considered for future policy and safety practices on campuses. • We test if the typical seasonal patterns of crime emerge on a university campus when there is a large shift in the population during the school year. • We compare the trends of crime in the university area (which is integrated into the downtown) to the rest of the city over the course of eight years. • Assault increases significantly in the university area at the beginning of the school year, but returns to expected patterns soon after. • Our study suggests that universities and colleges may want to target crime prevention efforts to the first few weeks of classes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".