Canadian environmental policy and politics : prospects for leadership and innovation
Bibliographic record
Abstract
TABLE OF CONTENTS DEDICATION INTRODUCTION (DEBORA L. VANNIJNATTEN AND ROBERT BOARDMAN) PART I - CANADIANS AND THEIR ENVIRONMENT 1. - The Environmental Movement in Canada (Robert Paehlke) 2. - The Green Vote in Canada (Steven D. Brown) PART II - MAKING ENVIRONMENTAL LAW AND POLICY 3. - The Courts and Environmental Policy Innovation (Marcia Valiante) 4. - Policy Instruments in Canadian Environmental Policy (Mark Winfield) 5. - Defining Effective Science for Canadian Environmental Policy Leadership (STEPHEN BOCKING) 6. - Spinning Wheels and Losing Traction (Glen Toner and James Meadowcroft) PART III - ENVIRONMENTAL GOVERNANCE AT MULTIPLE LEVELS 7. - The North American Context (Debora L. VanNijnatten) 8. - Sustaining Canadian Cities (Mary Louise McAllister) 9. - Aboriginal People and Environmental Regulation (Graham White) 10. - Environmental Governance at Multiple Levels (Robert Boardman) PART IV - ENVIRONMENTAL POLICY CASES 11. - The Failure of Canadian Climate Change Policy (Douglas Macdonald) 12. - Renewable Electricity (Ian H. Rowlands) 13. - From Old to New Governance in Canadian Forest Policy (Michael Howlett, JEREMY RAYNER, AND CHRIS TOLLEFSON) 14. - The Politics of Extinction (Stewart Elgie) 15. - Water Pollution Policy in Canada (Carolyn Johns and Mark Sproule-Jones) 16. - Mercury Science-Policy Debates (Bruce Lourie) 17. - Reform, Not Revolution (Sarah B. Pralle) APPENDIX: SELECT BIBLIOGRAPHIES FOR ENVIRONMENTAL ISSUES CONTRIBUTORS INDEX
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.078 | 0.010 |
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".