Green Edge Outreach Project: A large-scale public and educational initiative
Bibliographic record
Abstract
Abstract A collective outreach approach is fundamental for a scientific project. The Green Edge Project studied the impact of climate change on the dynamics of phytoplankton and their role in the Arctic Ocean, including the impact on human populations. We involved scientists and target audiences to ensure that the communications strategy was in agreement with scientists and audience requirements. We developed websites (academic site and blogs and an educational platform). Then, we produced a 52-minute documentary, ‘Arctic Bloom’, and infographics were created to explain experiments on the ice. We also organised a photo exhibition and live videos that enabled primary school-age students to ask questions directly of scientists working on the research icebreaker. Finally, both students and professionals drew their own conception of Arctic science, and our social media sites reached diverse groups of people. The evaluation results showed a large number of education structures (approximately 8000 schools and 104 museums or educational organisations) engaged with our communications outputs and encouraging statistics about website visits (117 021 and 3739 visits on the blog and the YouTube channel, respectively). Selecting different, but intersecting techniques, to promote a better understanding of the science contributed to the success of the communication and outreach outputs of the 3-year project.
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.
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 | 0.015 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".