Social Marketing to Protect the Environment: What Works
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it