The Reasonable Justice: An Empirical Analysis of Frank Iacobucci's Career on the Supreme Court of Canada
Notice bibliographique
Résumé
The Reasonable Justice: An Empirical Analysis of Frank Iacobucci's Career on the Supreme Court of Canada Benjamin R.D. Alarie Faculty of Law, University of Toronto. Andrew Green Faculty of Law, University of Toronto. I Introduction There are two widely shared views of Frank Iacobucci as a justice of the Supreme Court of Canada. The first is that he was a liberally inclined justice, particularly in the area of criminal law.1 That he has conventionally been regarded as a liberal despite being appointed in 1991 by the Progressive Conservative prime minister Brian Mulroney raises a number of questions. Is this conventional view of Justice Iacobucci actually correct? That is, is it borne out by his voting record over his more than thirteen years on the Court? If so, does this 'left of centre' claim hold fast beyond criminal law and extend to other areas of law? If it were the case that Justice Iacobucci was consistently left of centre, then it could be claimed either that Mulroney misapprehended his chosen nominee's political leanings or that he made his choice on the basis of factors – such as merit – unrelated to Justice Iacobucci's political inclinations. Another possibility, perhaps less likely, is that Mulroney actually preferred to appoint someone who would be somewhat left of centre. Yet another possibility is that Mulroney accurately assessed Justice Iacobucci's political leanings as a conservative at the time of his appointment, but that Justice Iacobucci shifted to the left over the course of his tenure on the Court. A priori, these explanations are all more or less plausible. [End Page 195] The second image of Justice Iacobucci is as a justice committed to building consensus on the Court by encouraging his fellow justices to reach agreement with him and with each other in deciding appeals.2 This second view also raises a series of questions. Was Justice Iacobucci the 'swing' justice on the Court in that the other justices needed to have him onside to form a winning coalition? Alternatively, was he part of a natural coalition on one side of most issues and able to persuade other, disinclined justices to join his view? Did his relative position on the Court shift depending upon the area of law at issue? Did Justice Iacobucci's leanings or preferences change over time? This article addresses these and other related questions using an empirical analysis of Justice Iacobucci's time at the Supreme Court of Canada. It is based on a database of all Supreme Court of Canada decisions heard from the beginning of the Court year in which he was appointed, September 1990 (he was appointed on January 7, 1991), to the time he retired from the Court at the end of June 2004. In Part ii, we briefly describe the database and the coding that was necessary to allow us to examine the voting records of different justices across different areas of law. In Part III, we turn to Justice Iacobucci's voting record. We use two methods, one direct and the other indirect, to analyse how he and his fellow justices voted. The first method attempts to measure judicial preferences directly by characterizing voting in a number of areas (Aboriginal law, Charter appeals, criminal law, labour law, and tax) as either liberal or conservative, depending on the identities of the appellants and respondents.3 The second method avoids coding particular appeals and votes as [End Page 196] either 'liberal' or 'conservative' and instead uses a method popularized by Kevin Quinn and Andrew Martin involving Bayesian inference and Markov chain Monte Carlo analysis to determine the posterior distribution of the 'ideal point' (assumed to be a random variable) of each justice on a policy spectrum.4 We use both methods in conjunction to analyse how Justice Iacobucci voted in general and by area of law in relation to his colleagues on the...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,006 | 0,012 |
| Études des sciences et des technologies | 0,031 | 0,009 |
| Communication savante | 0,010 | 0,003 |
| Science ouverte | 0,004 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».