Judicial Audiences: A Case Study of Justice David Watt’s Literary Judgments
Bibliographic record
Abstract
In 2016, the government of Canada announced reforms to the federal judicial appointment process aimed at increasing openness and transparency in the process. As part of those changes, all applicants for appointment to, or elevation within, the federal judiciary are required to submit responses to a new questionnaire. Applicants are asked to reflect upon the role of the judiciary in Canada’s legal system. One of the questions they are asked is: “Who is the audience for decisions rendered by the court(s) to which you are applying?” While the audience for a court’s decisions is not a new matter of academic and professional discussion, this recent change has brought judicial audience more squarely into the public eye. Nearly every successful applicant, of those whose responses are available, highlighted three key constituencies that should be addressed in every court decision: the parties, the public, and the legal profession. Justice David Watt’s short, staccato style introductions to decisions, authored since his elevation to the Court of Appeal for Ontario, have received attention. His introductions, which differ from the conventional style of legal judgments, have been the subject of legal blogs, mainstream media articles, and professional praise and criticism. Decisions that include intentional stylistic departures from conventional judicial writing are sometimes referred to as literary judgments. These so called literary judgments, including the ones written by Justice Watt, raise particular issues regarding the notion of judicial audience. Justice Watt’s departure from the conventional style of legal writing, particularly given the gruesome and tragic facts involved in many of the decisions he has written, raises numerous questions: Who is the audience for these literary judgments? Do judges write for a different readership when they issue decisions which depart significantly from the traditional style of legal writing? What are some of the attendant risks of delivering literary judgments to particular audiences? Do Justice Watt’s literary judgments speak appropriately and productively to the three constituencies for court decisions identified by judges themselves: the parties (understood broadly), the public, and the legal profession? Using Justice Watt’s decisions as a case study, this article considers the issue of judicial audience in the context of literary judgments. The article proceeds in three sections, each dedicated to an examination of Justice Watt’s literary decisions in relation to one of these three audiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".