Courts and Federalism: Judicial Doctrine in the United States, Australia, and Canada
Bibliographic record
Abstract
Courts and Federalism: Judicial Doctrine in the United States, Australia, and Canada, Gerald Baier, Vancouver and Toronto: UBC Press, 2006, pp. 207. Is everything old new again? Gerald Baier's insightful book brings back into the mainstream a long neglected examination of federalism from the perspective of judicial review. His analysis of the courts' impact on the development of federalism involves a detailed study of division of powers jurisprudence in the United States, Australia, and Canada. In each of these countries, Baier argues, the decisions of the highest courts continue to affect the shape of federalism, but his central claim turns on how these decisions are made. For Baier, judicial doctrine plays a significant role in influencing the reasoning of the courts and must be considered an independent variable worthy of study in its own right. Many scholars have debated the significance of doctrine on judicial decision making. However, Baier takes issue with scholars who, on the one hand, have characterized doctrine as a tool of objectivity and certainty, and those, on the other hand, who view doctrine as entirely political in nature (27). For Baier, doctrine is neither of these but it is “distinctly legal in character” and it is this legal reasoning that shapes outcomes (27).
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".