Distinguishing Expert Determination from Arbitration: The Canadian Approach in a Comparative Perspective
Bibliographic record
Abstract
The distinction between expert determination and arbitration is significant because a different normative regime applies to each, often leading to quite different outcomes for a given set of circumstances. A review of the jurisprudence in Canada shows that the two-step test developed by the Supreme Court of Canada to distinguish arbitration from expert determination:, in the 1998 case Sport Maska Inc. v. Zittrer, has too frequently been simplified to a one-criterion test: is the neutral deciding a formulated dispute (suggesting arbitration), or rather filing a gap in a contractual term (suggesting expert determination)? This approach, while adequate for distinguishing non-contentious expert valuation from arbitration, fails to recognize that a dispute may be submitted to a neutral for expert adjudication, a process that is quite different from arbitration. A comparative study of decisions from around the common-law world suggests that two factors can usefully serve to distinguish arbitration from expert adjudication: the duty of an arbitrator to adjudicate between the competing arguments of the parties (without being able to rely on his or her own subjective opinion, as can an expert adjudicator) and the related duty to comply with rules of procedural fairness.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.019 | 0.033 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".