Bibliographic record
Abstract
Mr. Big operations (“MBOs”) are a Canadian invention, a version of which dates back over 120 years, with its modern use beginning in the 1990s. However, it was not until 2014, with the Hart decision, that the Supreme Court of Canada found occasion to subject MBOs to regulation. The question this paper endeavours to undertake is whether the court’s new analytical framework, which treats MBO confessions as presumptively inadmissible, has affected the scripting of MBOs – or if there remains a proliferation of the same basic plot points across multiple scenarios. In analyzing the 14 cases in which the MBO took place post-Hart, four of which in-depth – Buckley, Dauphinais, Rockey, and Caissie – the author concludes that Hart has had no meaningful impact on MBO scripting, apart from superficial changes regarding the criminality of the fictional organization the suspect is recruited into, and the level of direct violence utilized. The coercive, manipulative tactics used by MBOs which can induce false confessions remain embedded within the technique. MBOs by their very nature remain problematic, and Hart’s legal tinkering has not defused their potential for wrongful convictions and abuse of process. However, despite the merits of MBO abolition, this is unlikely to occur anytime soon. As such, the author proposes several interim MBO reforms: (1) greater external oversight; (2) re-invigorating the abuse of process analysis; and (3) treating MBOs as akin to in-person interrogations.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".