Bibliographic record
Abstract
Members of the Toronto Police Services serve and protect Torontonians. They engage in “carding,” which is the act of randomly stopping people and getting their information to create a database that later helps in police investigations. The Toronto police contend that these random stops alleviate crime and keep residents of Toronto safe. However, the outcomes of carding are grossly disproportionate with the aims of the practice. Studies reveal that 44% of people of colour reported being stopped by the police at least once whereas only 12% of white people reported being stopped by the police. The overrepresentation of people of colour in these procedures shows that racial profiling occurs in the system. Black Torontonians are stopped and questioned by the police not necessarily in relation to a crime, but based on assumptions and stereotypes about people of colour. This research is founded in various scholarly secondary sources as well as news articles on the topic. The goal of this research was to explore the broader implications of racial profiling in the daily lives of people of colour and how this might contrast to the dominant group in society. This research examines gender, race, and social class in relation to police carding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".