Waiting for Globalization: An Empirical Study of the McLachlin Court's Foreign Judicial Citations
Bibliographic record
Abstract
A burgeoning literature celebrates the emergence of a global community of judges and a resulting international cross-fertilization of jurisprudence, especially as it bears upon constitutionally entrenched rights. This paper explores the Supreme Court of Canada's citations to judicial authority since 2000, and in more general terms its citations patterns since 1949, to see whether and to what extent this supports the notion of a growing of law. The paper argues that the notion of non-Canadian citation must be disaggregated into three component parts - English, American, and everything else - before it can usefully be examined, these three exhibiting quite different patterns; and it concludes that in none of them can the expanding globalization thesis be sustained. As well, it finds that the practice of the citation of non-Canadian authority is increasingly practiced by a single member of the Court, rather than being diffused across its entire membership. Finally, it looks at the kinds of cases that tend to include non-Canadian citations, and suggests that not only are we still waiting for globalization, but to the extent that we are focusing primarily on rights-based jurisprudence, we may also be looking in the wrong place.
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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 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.027 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".