Does Patronage Matter? Connecting Influences on Judicial Appointments with Judicial Decision Making
Bibliographic record
Abstract
Abstract.The federal government's power to appoint judges has come under increased scrutiny in recent years. While many suggest that partisan affiliation, gender and professional background may be influencing the Canadian appointment process, and some have called into question the fairness of such influences, little attention has been directed at determining whether these characteristics influence the outcome of cases. This paper studies decisions made by the Ontario Court of Appeal between 1990 and 2003 and uses a unique measure of partisan affiliation in an attempt to answer the question: do characteristics which play a role in the appointment process influence judicial decision making. Résumé.Ces dernières années ont vu une augmentation de l'attention donné à l'autorité du gouvernement fédéral en ce qui concerne la nomination judiciaire. Il y en a plusieurs qui suggèrent que l'affiliation partisan, le sexe, et l'expérience professionnelle des candidats judiciaires sont tous des caractéristiques qui peuvent influencer la procédure de nomination. Encore d'autres ont remis en question l'équité d'un choix basé sur ces influences. Cependant, la question qui n'a pas reçu beaucoup d'attention jusqu'à maintenant est si ces caractéristiques influencent le résultat des affaires juridiques. L'article qui suit examine les décisions rendu par le Cour d'appel de l'Ontario entre les années 1990 et 2003, employant une mesure unique d'affiliation partisan, avec le but de répondre à la question : Est-ce que les caractéristiques qui peuvent jouer un rôle dans la procédure de nomination influencent les décisions judiciaires?
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".