Bibliographic record
Abstract
Reasonable-person psychological detention is an area of criminal law that has been subject to a number of jurisprudential innovations in the 21st century. This work responds to a current gap in the literature regarding the importance of socioeconomic factors to the crystallization of detention in accordance with s. 9 of the Canadian Charter of Rights and Freedoms. The thesis of this paper is that socioeconomic factors are foundational to understanding the social context in which police interactions sometimes crystallize into detention. The socioeconomic aspect of social context reveals the way police interact with individuals in a certain space. Racial aspects of social context are postulated to be tied to socioeconomic aspects insofar as the racialization of individuals tends to occur in certain spaces – namely, high-crime, low-income neighbourhoods. The methodology of this work includes an analysis of trends in detention case law beginning with the 2009 decision of R v Grant and ending with the 2020 decisions of R v Thompson and R c Dorfeuille. Secondly, this work investigates the Honourable Michael H. Tulloch’s Report of the Independent Street Checks Review. Thirdly, this work investigates a series of studies conducted by Yunliang Meng, a geography scholar who analyzed the Toronto Police Service’s racialization of individuals as a function of space. In conclusion, this paper recommends modifications to police practices that require officers to make explicit statements at the outset of interactions with individuals which determine whether or not the individual is detained.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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 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".