Review of Donald R. Songer, 'The Transformation of the Supreme Court of Canada: An Empirical Examination' (University of Toronto Press, 2008)
Bibliographic record
Abstract
Donald R. Songer, an American political scientist, highlights in the introduction of his recent book, 'The Transformation of the Supreme Court of Canada: An Empirical Examination,' that he is not Canadian and has no legal training. Readers inclined to be uncharitable might take this admission as evidence that Songer is ill-suited to carry out the task of analyzing the Supreme Court of Canada in a subtle or careful way. The unfairness of such a snap judgment is obvious. Indeed, anticipating this concern, Songer himself claims that his 'outsider' status possibly confers the advantage of 'a perspective that may be somewhat different from those of ‘insiders’ and thus help to cast new light on some recurring themes in discussions of the Supreme Court of Canada' (p. 11). This may well be the case; after all, many Canadian observers and commentators have criticized the legitimacy of the Court’s decision-making in particular cases or its role more generally out of a normative distaste for the results of the Court’s toil. Songer himself claims to be in a position to be able to avoid these normative concerns. This book review assesses the extent of Songer's success in casting new light on the Supreme Court of Canada with the benefit of a more disinterested and external perspective.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".