Bibliographic record
Abstract
Preface Part I: An Overview of Collaborative GovernanceIntroduction: Stepping In-The Context for Collaborative Governance 1. Collaborative Governance and Collaborative Governance Regimes Part II: The Integrative Framework for Collaborative Governance2. Initiating Collaborative Governance: The System Context,Drivers, and Regime Formation Case Illustration: National Collaborative for Higher Education3. Collaboration Dynamics: Principled Engagement, SharedMotivation, and the Capacity for Joint Action Case Illustration: The Everglades Restoration Task Force,by Tanya Heikkila and Andrea K. Gerlak 4. Generating Change: Collaborative Actions, Outcomes, andAdaptationCase Illustration: The Military Community CompatibilityCommittee Part III: Case Studies of Collaborative Governance Regimes5. Who Speaks for Toronto? Collaborative Governance in theCivic Action Alliance, by Alison Bramwell 6. Collaborative Governance in Alaska: Responding to ClimateChange Threats in Alaska Native Communities, by Robin Bronen7. Power and the Distribution of Knowledge in a LocalGroundwater Association in Guadalupe Valley, Mexicoby Chantelise Pells Part IV: Collaborative Governance Regimes8. Moving from Genus to Species: A Typology of CollaborativeGovernance Regimes 9. Assessing the Performance of Collaborative GovernanceRegimes Conclusion: Stepping Back, Stepping Up, and Stepping Forward-Summary Observations and Recommendations Glossary ReferencesAbout the Authors and ContributorsIndex
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.007 |
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".