Effective approaches for community engagement and behaviour change
Bibliographic record
Abstract
Community-based behaviour change is widely considered as a key component of progressing sustainability. Increasingly, educational engagement programs are being delivered to build the capacity of community members to adopt more sustainable behaviours. But just how successful is this approach in achieving long term change? To try and answer this question, the National Centre for Sustainability (NCS) at Swinburne University of Technology conducted a longitudinal evaluation of the City of Whitehorse's Sustainable Ambassadors program. For the last two years, this innovative program has trained community members to become 'change agents' within the context of their peer network of friends, neighbours, or workplace. Participation in the program requires each community member to learn about behaviour change theory and practice and then design and deliver community-based behaviour change projects. The evaluation process included a survey of twenty five Sustainable Ambassador participants (56% response rate). Key research findings indicated: (1) 72% of participants self-reported their project as successful in achieving 'change'; (2) Targeting diverse community groups is an effective approach for integrating sustainable behaviour through the community; (3) Individuals who are organised in a group of like-minded people are more likely to remain committed to ongoing sustainability activities; (4) The majority of participants continued sustainability projects after their participation in the program; and (5) Networking opportunities are important in supporting ongoing change after program completion.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".