Digital storytelling – using multi-media tools to explore transformation processes in Arctic permafrost landscapes
Bibliographic record
Abstract
PermaRisk is a young investigator’s research group that simulates erosion processes in permafrost landscapes under warming climate and conducts risk assessments for ecosystems and infrastructure within the Arctic. Diverse ecological, social, and financial risks are associated with potential damages to ecosystem functions and infrastructure caused by permafrost thaw. Communication with local stakeholders in the Arctic such as the Bureau of Land Management in Alaska or town communities in Canada are integral to the research of PermaRisk. Local indigenous knowledge will help researchers to better understand past and current landscape changes and their impact on local life and infrastructure. \n \nPermaRisk promotes a transparent and open communication between research and society. Here, we present the tool of digital storytelling and how it is used to portray both the stories of the research project and the scientists as well as the stories of the people affected by climate change in the Arctic. Digital storytelling allows the combination of photos, videos, sound bites, interviews, graphics, maps, and data into compelling, entertaining, and interactive stories. Research data and materials brought back from fieldwork are used to look into questions like: What drives these scientists to do what they do? How do they do it? Why does it matter? To whom does it matter? How are local communities affected by Arctic climate change? How do they perceive the change and the research? \n \nIn the final product, the project’s main research findings are translated into accessible storylines about erosion and permafrost and placed within the socio-ecological context of climate change in the Arctic and globally. Finished Stories will be used for community outreach and public relations to advocate science. Furthermore, they will be integrated into teaching at German universities and schools to invite interactive learning and situate the research in concrete, real-life situations and communities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".