Can a Tribunal’s Former Counsel Appear Before the Tribunal? A Comment on <i>Certain Container Chassis</i>
Bibliographic record
Abstract
Lawyer mobility has been recognized as an important but not determinative consideration in legal ethics, particularly when it comes to conflicts of interest. Mobility poses particular issues for counsel to a tribunal. Those counsel may well at some point leave that position and pursue other opportunities. Prospective opportunities may sometimes involve appearing as counsel for a party before the same tribunal – especially where the tribunal operates in a highly specialized area of law. Can a lawyer appear before a tribunal if they were previously counsel to that tribunal? This discrete issue, though it rarely arises in the case law, presents unique considerations for analysis at the intersection of administrative law and legal ethics. In this comment, I analyze and critique the reasons of the Canadian International Trade Tribunal in Certain Container Chassis for declining to remove such a lawyer from a matter before it. I reconceptualize the Tribunal’s analysis into two separate questions and then add a third question. I conclude that, aside from confidentiality issues, a context-dependant analysis is preferable to an absolute rule.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.049 | 0.029 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".