What Nearly a Quarter Century of Experience Has Taught Us About Leon and "Good Faith"
Bibliographic record
Abstract
When the Court decided Leon, and even Sheppard, Krull, and Evans, it offered several interlocking rationales to support those decisions. Lower courts faced with the task of applying the good faith exception to other situations might be well-served by testing the possible results under the several models for interpreting Leon that are fairly drawn from the Court's opinions. To the extent that a particular result is consistent with all or most of those models, it is very likely the correct result. To the extent that the several models point to different conclusions, courts might consider identifying the model or models perceived to be at the heart of Leon and its progeny. The Court, on the other hand, now has the benefit of viewing nearly a quarter century of courts grappling wjth the good faith exception in various contexts. In some of those contexts, models suggested by the Court's opinions produce inconsistent results when applied to certain situations. Certainly the Court is not done with the good faith exception. One would hope that the next such case that comes before the Court will provide the Court with an opportunity to clarify or prioritize the various models in order to guide courts and litigants in future cases.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".