The uses of offender profiling in the Canadian Criminal Justice system
Bibliographic record
Abstract
Research about offender profiling appears to be predominantly conducted in the USA and in the UK while other countries seem to be left behind. This dissertation will seek to explore the uses of profiling in the Canadian Criminal Justice System. This study will be conducted as a structured literature review with the use of secondary data such as academic publications, cases details, and websites of Canadian criminal justice organizations. This removes any ethical problems such as anonymity, confidentiality or voluntary participation. This dissertation will seek to determine what are the general principles of profiling via the study of the three main approaches of profiling which are the FBI, Statistical and Clinical methods, before focussing more specifically on the ways profiling is used in the Canadian Criminal Justice system. The information gathered will show that profiling is used in two different aspects of the Canadian criminal justice system. First profiling is mostly used as a technique in order to help furthering an investigation when traditional methods have not led to the designation of a suspect. Profiling cannot solve a case by itself but it can potentially help a case by providing other information. This study also highlight that more recently profiling has also been used in Canadian courts as scientific evidence, but so far this remains marginal. The review of Canadian cases show that most of the time profiling evidence tends to be rejected especially when they take the form of behavioural profiling because of their lack of scientific grounds and thus their inability to comply with the legal requirements. However, profiling evidence based on crime scene analysis tends to be admitted to some extent as long as they meet the requirements established by the Courts.
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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 | 0.020 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.033 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".