Courtroom Technology Competence as a Lawyer’s Ethical Duty: What Should Regulators Do About It?
Bibliographic record
Abstract
Courtroom technology has become a common feature of many litigators’ practices. To be sure, the available technological tools vary greatly between courtrooms, ranging from relatively simple devices like audio-recording equipment or video screens on which evidence can be displayed to fully outfitted “e-courtrooms” that feature cutting-edge technology to assist in all aspects of trial proceedings. Notwithstanding this variance, there is now a strong case that lawyers need to understand and use an increasing number of technologies in order to effectively represent their clients in court.This Chapter considers whether the emerging ubiquity of courtroom technology translates into an ethical duty for litigators to have appropriate competence in relation to courtroom technology. The position ultimately taken is that courtroom technology competence is properly understood as an ethical obligation for litigators and should be a priority item for lawyer regulators. However, it is also argued that this ethical obligation should not be primarily addressed under the conventional rules-based system whereby lawyers’ behaviour is reactively evaluated against minimum standards within a “quasi-criminal” lawyer disciplinary regime. Instead, it is argued that lawyer regulators ought to adopt policy approaches that focus on facilitating and encouraging best practices when it comes to lawyers’ competence in courtroom technology.
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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 | 0.063 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.057 |
| Scholarly communication | 0.035 | 0.037 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.043 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".