Toward a New Regulation on the Exclusionary Rule. What Can Mexico Learn from American and Canadian Experiences?
Bibliographic record
Abstract
For over a century, U.S. debate surrounding the Exclusionary Rule has centered on questions such as if it inevitably allows criminals to go free, if the victims suffer due to police misconduct, or if the rule’s scope has narrowed over time. The rule’s primary objective has been to deter police misconduct. In Canada, discussions have focused on the necessity of conducting a discretionary analysis to determine the admissibility of unconstitutionally obtained evidence, especially since Canada’s Charter of Rights and Freedoms publication in 1982. Mexico’s debate on the issue began in 2008, and its version of the rule aims to guarantee more rights for the accused; however, it remains subject to legal interpretation and hasn’t been able to effectively reduce police misconduct. This article provides a brief comparison of the Exclusionary Rule’s-related regulation in the three neighboring countries. It suggests that Mexico should consider its northern neighbors common law legal history and pursue substantive changes to the National Criminal Procedures Code. These should directly address illegal evidence’s prohibition and any exceptions to the rule that may exist.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".