Policing in the Shadow of Legality: Pretext, Leveraging, and Investigation Cascades
Bibliographic record
Abstract
Police officers often exercise their authority at the boundary of legality. Two of policing’s features contribute to this tendency: first, the scope of certain police powers is unclear; second, officers enjoy broad discretion to initiate proactive police encounters. This article argues that these interrelated features of policing result in three law enforcement phenomena: pretext, leveraging, and investigation cascades. Pretext denotes that police officers invoke lawful justifications to pursue unlawful aims. Leveraging implies that officers exploit individuals’ psychological vulnerabilities to secure compliance or to receive consent to engage in more intrusive investigatory tactics. Investigation cascades occur when officers gather information through police powers with low burdens of proof to exercise more invasive investigation tactics with stricter burdens of proof. This article demonstrates how criminal procedure fails to adequately protect individuals against pretext, leveraging, and investigation cascades. It concludes with a set of concrete proposals to address these three law enforcement phenomena.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".