The SCC's Dilemma: What to Do with Interveners?
Bibliographic record
Abstract
At a conference in 2016, Osgoode Hall Law School Dean Lorne Sossin made the following offhand comment: “I think it is possible to tell the most important Supreme Court of Canada cases by the number of interveners that were involved.” I assume what he meant--and granted, it was somewhat tongue in cheek--that the more interveners there are in a case, the more important the case.\nThe comment intrigued me. Is it true? It is such a simple proposition. Intuitively, it seems right: more parties would wish to involve themselves in those cases that have larger impacts, or that represent more important state matters. But it seemed such a throwaway line at the time ...\nAnswering this question became part of a larger, ongoing project to assess the importance of interveners at the Court, from the very first intervention in the 19th century to the present day. It attempts to assess the actual role interveners may play by posing a series of questions. What effect, if any, do interveners have on the judges' decisions? How can these effects be measured? Quantitatively? Qualitatively? Since there may be dozens of interveners in any given case, it means, over the years, more interveners have appeared than *80 parties. And yet their role is not well understood; interveners operate largely in the shadows of a case, known mainly to a few lawyers and scholars who follow the Supreme Court's jurisprudence. Surprisingly little analysis on them has been done or written.
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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 | 0.074 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.057 |
| Scholarly communication | 0.019 | 0.034 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.047 | 0.047 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".