Self-Represented Litigants, Active Adjudication and the Perception of Bias: Issues in Administrative Law
Bibliographic record
Abstract
This paper advocates for a more active role for adjudicators, one in which they provide direction to parties and actively shape the hearing process. Indeed, active adjudication can be an important access to justice tool. Without some direction and assistance from the adjudicator, growing numbers of self-represented litigants cannot meaningfully access administrative justice. Importantly, however, as the role of the adjudicator shifts, so too must our understanding of the notion of impartiality. If it is unfair to expect self-represented litigants to navigate the hearing process without adjudicative assistance and direction, it is also unfair to insist on a vision of impartiality that prevents adjudicators from actively managing the hearing process. To that end, the author develops the notion of “substantive impartiality” to show how existing legal principles can accommodate a more active role for the administrative adjudicator. The author also makes practical recommendations and suggests how administrative tribunals can help self-represented litigants understand the principles and procedures related to bias allegations.
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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 | 0.042 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.089 |
| Scholarly communication | 0.030 | 0.021 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.011 | 0.010 |
| 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".