UPROOTING THE CELL-PLANT: COMPARING UNITED STATES AND CANADIAN CONSTITUTIONAL APPROACHES TO SURREPTITIOUS INTERROGATIONS IN THE DETENTION CONTEXT
Bibliographic record
Abstract
This article examines judicial approaches to cell-plant interrogations in Canada and the United States. These are surreptitious interrogations whereby the police inject an undercover state agent into the detention environment with the goal of eliciting inculpatory statements from an accused. The article analyzes and compares the strengths and weaknesses of the applicable legal tests emanating from the right to silence found in section 7 of the Canadian Charter of Rights and Freedoms and the Sixth Amendment right to counsel of the U.S. Bill of Rights. Although in both countries, an accused may seek to have their incriminating statements excluded from evidence where they successfully persuade the court that such statements were elicited by an undercover state agent, police have managed to exploit loopholes currently embedded within the current legal tests. This has consequently led to the admission of incriminating statements and subsequent convictions thus undermining the importance of the relevant constitutional protections. Through a comparative study, this article proposes how to strengthen the current legal tests by closing the loopholes which permit state actors to undermine constitutional protections afforded to criminal defendants.
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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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.035 | 0.037 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".