Negative Spotlight: Event-Driven Effects on Support for the Canadian Supreme Court
Bibliographic record
Abstract
Abstract Longstanding public support for the Supreme Court of Canada is well-documented and contributes to its public legitimacy. However, the sources of this support and how vulnerable it may be to political factors or negative coverage of events are not well understood. In February of 2023, Justice Russell Brown took a leave of absence from the Supreme Court following a conduct complaint under review by the Canadian Judicial Council. Justice Brown retired from the bench in June of that year, before the CJC concluded its investigation. In the intervening period, media coverage of the events that prompted the attention from the CJC thrust the Court into the spotlight. Using data from an original two-wave survey experiment ( n = 1,222) from May and November of 2023, we investigate whether perspectives toward the circumstances surrounding Brown’s retirement hurt perceptions of the Court’s legitimacy. We find that the event did not disrupt support for the Court over time but also point to the ways in which opinion toward the Court changed pre- and post-resignation. These findings suggest that support toward Canada’s high court is at present largely stable even in the case of negative coverage of a high-profile event. However, we also acknowledge the potential vulnerabilities that negative coverage of the Court may present.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".