L’aigle et le castor : étude de la distribution spatiale de la criminalité aux États-Unis et au Canada
Bibliographic record
Abstract
In the last three decades, a number of researchers have undertook the comparison of American and Canadian crime rates. Among them, Lipset (1990) and Hagan (1991) have shown that violence was more frequent south of the border than north of it. To explain why crime was more frequent in the US than in Canada, those authors argued that differences in values and culture of each country's residents was the principal determinant of this situation. Using regional and infranational disaggregated crime rates, this article shows that differences in both country's crime rates are not univocal. For example, crime rates in Canada are not higher than those of Northern United States for three crimes out of four studied. What makes US crime rates appear much higher than the Canadian ones can be attributable to a small number of States and cities which have extraordinarily high crime rates.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".