Bibliographic record
Abstract
Increasingly, adjudicators and administrative tribunals are interested in active adjudication. Adjudicators are on the front line of administrative justice, we see firsthand the difficulties self-represented and other litigants face as they try to navigate the legal system. We see the challenges associated with adversarial adjudication and we see how the role of the decision-maker can make a difference to the parties, their perception of the fairness of the proceeding, and (arguably) the legal outcome. Many of us see active adjudication as a way that adjudicators can help remove unnecessary barriers for all litigants. This paper addresses the actual practice of active adjudication. I attempt to explain, in specific terms, how adjudicator's can introduce active adjudication into their practice. I begin by explaining what active adjudication is and why it can improve access to justice. Second, I consider what tools are needed to become effective active adjudicators. Finally, I propose some best practices and practical suggestions.
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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 | 0.264 | 0.300 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.032 | 0.032 |
| Open science | 0.013 | 0.016 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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".