Sustainability issue communication and student social media engagement: Recommendations for climate communicators
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
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.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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