Sustainability issue communication and student social media engagement: Recommendations for climate communicators
Bibliographic record
Abstract
This study explores the digital and social media information habits and preferences of students, particularly as they concern issues-based communication relating to climate change and sustainability. Researchers surveyed 203 undergraduate students studying a wide range of subject areas in a small Canadian liberal arts style university. Results were analysed using basic statistics to determine broad trends in social and digital media use among participants, their assessment of what kinds of content they found engaging online and their preferences relating to searching and sharing information on news and issues. Different environmental messages were also assessed by participants for whether they were engaging. Participants used a wide variety of platforms, in diverse locations, but demonstrated a tendency to use Google and YouTube most often to search for issues about which they cared. Respondents indicated a preference for image or video-based content, and also indicated that images and videos made a website more attractive. They generally reported not sharing news on social media, and tended to rate environmental messages with a problem-solution framework as most engaging. This study suggests that climate-change related issue marketing should favour YouTube and other video content, and should pay close attention to how environmental messages are presented in order to be most engaging to their target audiences.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".