Bibliographic record
Abstract
Foreword Preface Section I: Introduction Chapter 1: Introduction: Fostering Sustainable Behavior Section II: Influencing Behaviors in the Residential Sector Chapter 2: Reducing Waste The Problem Potential Behavior Solutions Case: No Junk Mail (Bayside, Australia) Case: Decreasing Use of Plastic Bags and Increasing Use of Reusable Ones (Ireland) Case: Increasing Curbside Recycling of Organics (Halifax, Nova Scotia) Other Notable Programs Summary Questions for Discussion References Chapter 3: Protecting Water Quality The Problem Potential Behavior Solutions Case: Influencing Natural Yard Care (King County, Washington) Case: Scooping the Poop (Austin, Texas) Other Notable Programs Summary Questions for Discussion References Chapter 4: Reducing Emissions The Problem Potential Behavior Solutions Case: Anti-Idling: Turn it Off (Toronto, Canada) Case: TravelSmart (Adelaide, South Australia) Other Notable Programs Questions for Discussion Summary References Chapter 5: Reducing Water Use The Problem Potential Behavior Solutions Case: Reducing Water Use (Durham Region, Canada) Case: Ecoteams (United States, Netherlands, United Kingdom) Other Notable Programs Summary Questions for Discussion References Chapter 6: Reducing Energy Use The Problem Potential Behavior Solutions Case: The One Tonne Challenge to Reduce Greenhouse Gas Emissions (Canada) Case: ecoENERGY to Promote Home Energy Efficiency (Canada) Other Notable Programs Summary Questions for Discussion References Chapter 7: Protecting Fish and Wildlife Habitats The Problem Potential Behavior Solutions Case: Reducing Deliberate Grass Fires (Wales, United Kingdom) Case: Planting Eastern Shore Natives (Virginia) Case: Seafood Watch: Influencing Sustainable Seafood Choices (United States) Other Notable Programs Summary Questions for Discussion References Section III: Influencing Behaviors in the Commerical Sector Chapter 8: Reducing Waste The Problem Potential Behavior Solutions Case: Green Dot, Europe's Packaging Waste Reduction Case: Fork It Over: Reusing Leftover Food (Portland, Oregon) Case: Anheuser-Busch: An EPA WasteWise Hall of Fame Member Other Notable Programs Summary Questions for Discussion References Chapter 9: Protecting Water Quality The Problem Potential Behavior Solutions Case: Chuyen Que Minh, Reducing Insecticide Use Among Rice Farmers (Vietnam) Case: Dirty Dairying (New Zealand) Other Notable Programs Summary Questions for Discussion References Chapter 10: Reducing Emissions The Problem Potential Behavior Solutions Case: Bike Sharing Programs Case: ATT's & Nortel's Telework Programs (United States, Canada) Other Notable Programs Summary Questions for Discussion References Chapter 11: Reducing Water Use The Problem Potential Behavior Solutions Case: Conserving Water in Hotels (Seattle, Washington) Case: Fighting the Water Shortage Problem in Jordan Other Notable Programs Summary Questions for Discussion References Chapter 12: Reducing Energy Use The Problem Potential Behavior Solutions Case: Using Prompts to Turn Off Lights (Madrid, Spain) Case: Norms-based Messaging to Promote Hotel Towel Reuse (California) Other Notable Programs Summary Questions for Discussion References Chapter 13: Concluding Thoughts and Recommendations
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 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".