MétaCan
Menu
Back to cohort
Record W3122505542 · doi:10.1353/tlj.2007.0004

The Reasonable Justice: An Empirical Analysis of Frank Iacobucci's Career on the Supreme Court of Canada

2007· article· en· W3122505542 on OpenAlexvenueaboutno aff
Benjamin Alarie, Andrew Green

Bibliographic record

VenueUniversity of Toronto Law Journal · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSupreme courtLawEconomic JusticePoliticsSociologyVotingPolitical science

Abstract

fetched live from OpenAlex

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...

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.706

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.012
Science and technology studies0.0310.009
Scholarly communication0.0100.003
Open science0.0040.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.026
GPT teacher head0.269
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueUniversity of Toronto Law JournalSame topicJudicial and Constitutional StudiesFrench-language works237,207