Knowledge Mobilization, Citizen Science, and Education
Bibliographic record
Abstract
While climate change project funders, community partners, and researchers are increasingly calling for robust knowledge mobilization plans, including knowledge translation and transfer, there are ongoing debates about how to design and measure the effectiveness of these efforts for specific target audiences. Climate Change S.O.S. – Save Our Syrup! is a knowledge mobilization program that brings high school students out to a working sugarbush in Ontario, Canada. This program was developed by drawing on the outdoor education expertise at the Mountsberg Conservation Area, forestry specialists’ consultation, and the project team’s work on previous community-based studies. Students also contribute to a citizen science project monitoring the health of the sugar maple ecosystem and learn about the impact of climate change on this ecosystem. Pretest and posttest surveys measured the knowledge mobilization program’s effectiveness on the students’ knowledge, attitudes, and behaviors. With 600 grade 9–12 participants in this project, this is one of the largest studies that the team could find that measures climate change knowledge mobilization effectiveness on high school students. Results indicate short-term positive changes in knowledge of climate change and maple syrup, and positive changes in students’ attitudes regarding their ability to lessen their impact on climate change, but no statistically significant longer-term change to behavior. After highlighting some of the key issues and concerns around designing three projects and measuring effectiveness, the paper outlines how the program was developed, its key results and limitations and lessons learned. We argue that although single, targeted knowledge mobilization efforts can be effective, longer-term, multi-pronged approaches are likely necessary to contribute to sustained behavioral change.
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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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".