Adjudicating Uncertain Facts – The Case for Procedural Legitimacy
Bibliographic record
Abstract
This paper is a commentary on the legitimacy of judicial fact-finding in civil litigation. Judges are called on to make authoritative factual findings in conditions of evidentiary uncertainty and the decision-making process cannot guarantee the accuracy of those outcomes. Given the inevitable risk of error, on what basis is the authority of judicial fact-finding legitimate? My exploration into this question leads me to set out a notion of procedural legitimacy that bridges two unavoidable aspects of adjudication: evidentiary gaps leading to factual uncertainty/indeterminacy, and the need for justifiably authoritative dispute resolution. I show how the notion of procedural legitimacy enables a recognition that the civil litigation system, while inevitably imperfect, is nonetheless legitimate. The nuances of this claim are demonstrated by situating the procedural legitimacy theory within debates about the instrumental and noninstrumental values of litigation procedures, drawing on the work of Robert Bone and Ronald Dworkin, among others. The notion that procedural propriety in civil litigation systems is key to maintaining legitimate judicial outcomes gestures towards the important role that legal players have in ensuring adjudicative legitimacy. As such, this paper serves as a call on all legal actors, whether practitioners, policy-makers, academics or adjudicators, to reflect deeply on their roles in ensuring that cases are decided with procedural integrity because the legitimacy of Canadian civil litigation depends on it.
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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 | 0.081 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.119 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.026 | 0.031 |
| Insufficient payload (model declined to judge) | 0.003 | 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".