Visualizing Interrogative Injustice: Challenging Law Enforcement Narratives of Mr. Big Operations Through Documentary Film
Bibliographic record
Abstract
Documentary films can play a substantial role in visualizing key issues of legal significance concerning police interrogations and wrongful convictions. This article examines the ways in which the film, Mr. Big: A Documentary (MBAD) makes visible problems related to Mr. Big Operations (MBOs) in Canada. MBOs are undercover operations conducted by law enforcement officials for the purpose of eliciting incriminating information from an individual who they suspect has committed a serious crime such as murder. During these operations, undercover officers use strong financial and social inducements to entice targets to join the organization. In exchange for membership, targets are expected to reveal information about the crime they committed. This is usually contextualized as a demonstration of trust and/or in order for the organization to arrange to have another person admit to the crime. Law enforcement officials will also intimate their willingness to perpetrate violence on those who do not comply with their demands. This leads to targets feeling a sense of being threatened if they do not comply. Through numerous on-screen interviews with those who have expertise or are knowledgeable with respect to MBOs, MBAD attempts to accomplish several objectives. More generally, it stresses that there are dangers attached to MBOs that may lead to targets to falsely confess leading to wrongful convictions. Second, it scrutinizes problems associated with a particular Mr. Big scenario: targeting Atif Rafay and Sebastian Burns led to their convictions. Third, by shining a light on the problematic nature of MBOs and the Rafay-Burns case, MBAD effectively constructs law enforcement officials as questionable actors who knowingly and intentionally adopt these techniques.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".