Connecting students with the Arctic: Live classroom online teaching from the Arctic to the USA and Europe: five case studies
Bibliographic record
Abstract
Researchers working in polar regions face unique challenges not only in terms of environmental extremes and logistics, but also with respect to communications and commitments at their home institutions. With the increasing availability of highspeed internet at remote polar locations, some of these constraints are now diminishing. Polar scientists now have opportunities to communicate in realtime, including directly into universities, schools and public spaces. This paper explores the practicalities and implications of online teaching at institutions in the UK, USA, Germany and France, delivered from the remote Hamlet of Pond Inlet in the Canadian High Arctic by three of the authors. In this qualitative experiential study, the experience of an expedition to the region in early spring 2023 showed that, despite some remaining technical challenges, live teaching from remote locations in the Arctic to secondary, further and higher education institutions can engage with students. The technology to allow this is now a realistic but still limited option, providing students with an unprecedented learning experience. Such communications might help to inspire a new generation to understand and value polar regions.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".