Bibliographic record
Abstract
It is frequently said that no person is an island, and a similar comment applies to those who endeavour to write a book.We have benefited from the assistance of many individuals during the course of our work, and we owe a debt of gratitude to them.For important financial support, we gratefully acknowledge the University of the Pacific for several grants, including a Scholarly Activities Grant in 1997 (The Supreme Court Data Base Project) and two Eberhardt Faculty Research Grants in 2000 and 2004 (Leadership on the Supreme Court of Canada, and Economic Liberalism and the Business Decisions of the Supreme Court of Canada).We also benefited from the generous support of the Government of Canada's Department of Foreign Affairs through its Faculty Research Award program.Their funding supported our travel to Winnipeg and Ottawa to collect data on newspaper accounts of the ideology of Supreme Court nominees in the summer of 2004.We extend special thanks to Daniel Abele, the program officer for the Canadian Embassy, in Washington, DC, for his efforts in support of our work.As is customary with any grant project, the findings that we present represent our own views and should not be attributed in any way to the granting institutions.We are grateful for the permission from several journals to reprint portions of our previously published material.Chapter 4 presents some updated findings from our prior work on search and seizure cases: Matthew E. Wetstein and C.L. Ostberg, "Search and Seizure Cases in the Supreme Court of Canada: Extending an American Model of Judicial Decision Making across Countries," Social Science Quarterly 80 (1999): 757-74.Other significantly revised portions are derived from the following publications: C.L. Ostberg, Matthew E. Wetstein, and Craig R. Ducat, "Leaders, Followers, and Outsiders: Task and Social Leadership on the Supreme Court of Canada in the Early
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.542 | 0.287 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".