Bibliographic record
Abstract
The Institute of Intergovernmental Relations has a tradition of entering into partnerships for many of its projects.In this instance, we worked with a group of senior scholars at the University of Toronto.The theme for this book emerged in early 2012 from exchanges and conversations triggered by Richard Simeon following the opinion of the Supreme Court of Canada on a national securities regulator.As Richard, David Cameron, Carolyn Hughes Tuohy, and I contemplated holding a conference on the implications of the Court's opinion, we realized that we needed to look beyond that relatively narrow issue to consider what Richard called the changing landscape of Canada's political economy.Carolyn Tuohy then agreed to "represent" her colleagues in designing the conference and the publication, and the University of Toronto School of Public Policy and Governance became a cosponsor of the event.It is fitting that the book that results from those discussions and the conference is dedicated to Richard Simeon, the outstanding scholar of federalism and former director of the Institute, who passed away in October 2013.The dedication follows this preface.I am pleased that we avoided an obvious central Canada bias by asking Professor Loleen Berdahl, of the University of Saskatchewan, to be the lead on the conference and the lead editor of this book with the support of Carolyn Tuohy and myself.I thank both of them for being such good partners.Regional resource disparities and the tensions they generate are a perennial Canadian topic.Governments, unfortunately but not surprisingly, are reluctant to tackle these issues.This imposes an even greater responsibility on universities and think tanks to study and understand these issues and to disseminate the work of scholars whose chapters are in this book.On behalf of the editors, I would like to thank the authors for their contributions and the anonymous reviewers who commented on selected chapters.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.406 | 0.210 |
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".