Marijuana Arrests in Toronto Canada: A Look into the Canadian Criminal Justice System
Bibliographic record
Abstract
Marijuana related drug offenses made up fifty-eight percent of all Controlled Drugs and Substances Act offenses in Canada in 2016. On October 17, 2018, Canada legalized marijuana. As part of the efforts to legalize marijuana, descriptive statistics of single variables, like the age of the arrestees and the number of people arrested per year, were reported by the Toronto Star newspaper. The dataset analyzed in this research predates the legalization of marijuana and was collected from 1997 to 2002 on 5,226 individuals arrested in Toronto, Canada for simple possession of small quantities of marijuana. When an offender was arrested for possession of marijuana, they were either released with a summons, which summoned them to appear in court later, or they were held without release until their court date. Did the choice to release someone on a summons or not depend on the color of their skin? Did the sex, age, employment status, current citizenship status of the offender, year of the arrest, or number of previous convictions of the offender determine if they were released with a summons? Other questions that my research seeks to answer include the following. Did males and females have the same number of previous convictions? Did people of color have the same number of previous convictions as whites? Were the average ages of arrest the same for males and females? Was the average age of offenders the same for the years 1997, 1998, 1999, 2000, 2001, and 2002? These questions will be answered with parametric and nonparametric hypothesis testing. Graphical data displays, stratified bar charts and correlation plots, will be used to convey the findings. My results present an insight into the possible existence of bias in the Canadian criminal justice system enabling the justice system to consider if these biases are still present.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.022 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".