Judicial Audiences: A Case Study of Justice David Watt's Literary Judgments
Bibliographic record
Abstract
Applicants to the federal judiciary identify three main audiences for their decisions: the involved and affected parties, the public, and the legal profession. This case study examines a set of decisions authored by Justice David Watt of the Ontario Court of Appeal, involving the rape, torture, murder or attempted murder of women, in which he attempts humour or uses puns, parody, stark imagery and highly stylized and colloquial language to introduce the violence, or factual circumstances surrounding the violence, in these cases. It assess these introductions in relation to the audiences judges have identified as important for their decisions. The study concludes that these literary introductions may not speak productively to any of the three audiences identified as critical by applicants to the federal judiciary. For example, Justice Watt’s writing in these introductions does not reflect the empathy and sensitivity that some judges have identified as an important feature of writing that is intended for the parties (in their applications for appointment). The study also highlights two interrelated factors that judges should consider when writing decisions involving gender-based violence with a view to the public audience that these decisions are likely to receive. These factors are the crisis of public faith in the legal system’s ability to respond appropriately to incidents of gender-based harm, and the importance of writing judicial decisions that do not obscure the social context and dynamics that produce gender-based violence. Justice Watt’s unorthodox writing in these cases does not reflect consideration of these factors. Justice Watt’s short, staccato style introductions to decisions 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, 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? 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?
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.063 | 0.019 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".