Witness Preparation before Trial in Anglo-American Law: Aims, Dangers, and Remedies
Bibliographic record
Abstract
Witness preparation before trial constitutes one of the lawyers’ most important and fundamental tools in the practice of criminal law. It fulfills the lawyer's professional duties both towards their client and towards the court, and it also contributes to the effectiveness of the judicial process. Despite the centrality and importance of this practice, it creates ethical and evidentiary difficulties. Conducting such an interview is often accompanied by the fear that the interview will be abused and might serve as an improper means to guide and coach the witness out of court. The fears and dangers embodied in witness interviews highlight the need for an arrangement for both the ethical and evidentiary aspects involved. In this article, I discuss the purposes of witness preparation, the risks and difficulties that it entails, the regulation of this practice in Anglo-American law (the US, England, Israel, Canada, Australia and New Zealand), and I suggest a possible model arrangement that would, in my opinion, provide a comprehensive response to the concerns and difficulties this practice engenders and which can contribute to lawyers’ professionalism and promote the purposes of criminal procedure.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".