Bibliographic record
Abstract
In this article, I explore both the idea and practice of adjudicative ethics in the context of administrative justice in Canada. This is a large topic and one which is particularly timely as accountability, transparency and conflict of interest are all renewed areas of interest for governments across Canada. Elsewhere, I have suggested it is time to approach administrative justice as a justice system rather than as a disparate set of tribunals and boards. One way in which this coordination can be expressed is through a shared process of accountability for the conduct of adjudicators. My hope in elaborating adjudicative ethics is to provide a foundation for such an evolution of administrative justice.This analysis is divided into three sections. In the first section, I discuss what is distinct about adjudicative ethics (for example, distinguishing this field from the ethics of public servants or judicial ethics). In the second section, I consider some existing schemes which address adjudicative ethics and their limitations. In the third section, I canvass what remains unsettled and the challenges ahead.
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 | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.043 | 0.064 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".