Tribunal Independence and Impartiality: Rethinking the Theory after Bell and OceanPort Hotel — A Call for Empirical Analysis
Bibliographic record
Abstract
Is the approach currently taken by Canadian courts to determine the amount of independence that administrative tribunals require appropriate to fulfil the goals of providing administrative justice and encouraging public confidence? The author argues that it is essential to appreciate the modes of internal functioning and the normative understandings within administrative bodies in order to make a valid determination of the degree and nature of independence that they should have. For this, more qualitative empirical analysis is needed in our administrative law literature. This article begins with an overview of the rationale behind tribunal independence, outlining the current approach used by the courts in evaluating independence and impartiality on judicial review applications. It then moves to discuss some of the shortcomings of the judicial model and the utility of empirical data in evaluating questions of tribunal independence. It concludes by considering the Supreme Court’s decisions on tribunal independence and impartiality, Bell Canada v. Canadian Telephone Employees Association and its predecessor, Ocean Port Hotel Ltd. v. British Columbia (Gen. Manager Liquor Control), and evaluating whether these cases have affected the jurisprudential notion that there is significant value in “seeing the tribunal in operation.”
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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 | 0.033 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.022 | 0.108 |
| Scholarly communication | 0.031 | 0.027 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".