Undercover Investigation, Liquor Laws, and “Disreputable” Detectives in Late Nineteenth-Century Canada
Bibliographic record
Abstract
Undercover police operations have long been recognized as legally and ethically problematic, leading to court appeals and discourse about the appropriate uses of deception and manipulation by the state. The issue was debated as early as 1886, when a jury acquitted a defendant because the evidence was gathered using deceptive methods. In Sarnia, Ontario, a tavern owner named Charles Hand was accused of orchestrating the bombing of temperance leaders’ homes. The Crown’s case was based on the testimony of an undercover detective who had befriended Hand and his family. With the support of the trial judge, the defense discredited the testimony on the basis that detectives were thought to be disreputable people, a fact confirmed by their surreptitious investigations under the Canada Temperance Act. At issue was the question of how anyone could accept the testimony of a detective who had used lies and deception against reputable people. While, ostensibly, the Hand case was a victory of the “wets” over the “drys,” it was also a conflict over police modernization. In this case study, I argue that the employment of this novel investigative tactic challenged traditional, anti-modern, views about the appropriate limits of police behaviour, deception, and state authority.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".