Community-Based Social Marketing—Creating Lasting, Sustainable, Environmental Change: Case Study of a Household Stormwater Management Program in the Region of Waterloo, Ontario
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
With increased frequency of extreme weather events due to climate change, there is growing need for urban, small-scale adaptation and preventative measures such as stormwater management to reduce the risk of flooding. Homeowners are often reluctant to adopt preventative stormwater measures without tangible benefits or direct experience with the flooding risks or other negative externalities. Using community-based social marketing (CBSM) as a framework, we investigated how to more effectively encourage stormwater management at the household level. In collaboration with the Canadian non-profit organization, Reep Green Solutions (Region of Waterloo, Ontario), we focused on an existing program, the RAIN Home Visit (RHV), which was designed to increase engagement in pro-environmental stormwater management behaviors. Reports from the RHV were assessed, and past program participants were interviewed using a semi-structured question set to identify barriers encountered in enacting these behaviors and to assess the program for inclusion of CBSM principles and tools. Surveys were used to collect demographic data from participants. We found that while preferred behaviors were explained and incentives were provided, more thorough, clear explanation was needed for homeowners as well as incentives of suitable size and value to effectively motivate homeowners to change. Key features that should be included in future RHV programs are public commitments, follow-up, and reminders. Further research should consider risk perception impacts with CBSM, to determine how these can work together and, perhaps, which precedes the other. Some people may be more influenced by social norms to act and others by risk perception.
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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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