Public Crime Mapping in Canada: Interpreting RAIDS Online
Bibliographic record
Abstract
The capacity for the mapping of crime data has shifted to the digital world, allowing online public crime mapping and the dissemination of information to a broader audience. While the field of critical cartography has questioned the elements and underlying assumptions of various map types, public crime mapping has not been analyzed in the same manner. Through analysis of the design choices and omissions in online public crime maps that pertain to Hamilton, ON and London, ON, the concepts of critical cartography are applied to the largely ignored field of crime maps. While the existence of online public crime maps can potentially facilitate data sharing and analysis to better allow police forces to reduce the incidence of crime, there are also consequences of maps presenting incomplete or inaccurate information to their audiences, as was found to be the case for both cities that were analyzed. These consequences include effects on the public perception of crime, changing attitudes toward crime-induced fear, and negative implications for economic development in areas that might be seen as too high-risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".