Bibliographic record
Abstract
This article was written for Osgoode Hall Law School’s annual Consti- tutional Cases conference, and provides the keynote overview of the McLachlin Court’s 2014 constitutional jurisprudence. The Court’s 2014 constitutional decisions (Appointment and Senate References; Tsilhqot’in Nation; Trial Lawyers) and restrictions on Mr. Big operations (Hart), in combination with a tsunami of Charter decisions early in 2015 (the 2015 Labour Trilogy; Carter v. Canada; R. v. Nur; and others), made this a legacy-building year. More than an overview, this Article probes the nature of the McLachlin Court’s legacy this year and the relationship between legal and political dynamics, to ask: in light of Chief Justice McLachlin’s modest goals as leader of the Court and the immediate backdrop of the Nadon controversy, the political leadership’s attack on the Chief Justice, and poor relations between the institutions, should the jurisprudence of this period be seen as a passive response to events or a more active assertion of judicial authority? Whether this legacy-making jurisprudence was a function of serendipity or opportunity (and perhaps both) is an intriguing question that sheds light on the Chief Justice and her Court in what was a singular and unforgettable year on legal and political measures.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".