Bibliographic record
Abstract
ABSTRACT This paper was initially presented orally as the keynote address of the Chartered Institute of Arbitrators, Canada Branch, programme at Canada Arbitration Week in Toronto on 17 October 2023. The address explores how sitting arbitrators shall respond when a party, or persons evidently aligned with a party, make use of bespoke websites or social media to lodge false and defamatory accusations of arbitrator misconduct that, if true, would warrant disqualification and/or vacatur of any awards already issued on the basis of bias. Among the core observations is that the falsely accused arbitrator ought not to resign—as this is precisely the objective sought by the publisher of the accusations, and the tactic, if successful, seriously threatens the integrity of the arbitral process. A corollary of this observation is that the displeasure naturally felt by the accused arbitrator ought not be generally regarded by the institutions that regulate arbitrator conduct—whether provider organizations or courts—as a species of bias but as a normal human reaction to untoward behaviour by a party or its agents in the course of the proceedings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.074 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.036 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.041 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 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".