Arctic Tectonics and Volcanism: a multi-scale, multi-disciplinary educational approach
Bibliographic record
Abstract
Abstract. Geologically, the Arctic is one of the least-explored regions of Earth. Obtaining data in the high Arctic is logistically, economically, and environmentally expensive, but the township of Longyearbyen (population of 2617 as of 2024) at 78° N represents a relatively easily accessible gateway to Arctic geology and is home to The University Centre in Svalbard (UNIS). These unique factors provide a foundation from which to teach and explore Arctic geology via the classroom, the laboratory, and the field. UNIS was founded in 1993 as the Norwegian “field university”, offering field-based courses in Arctic geology, geophysics, biology, and technology to students from Norway and abroad. In this contribution, we present one of the educational components of the international collaboration project NOR-R-AM (a Norwegian-Russian-North American collaboration in Arctic research and collaboration, titled Changes at the Top of the World through Volcanism and Plate Tectonics) which ran from 2017 to 2024. One of the key deliverables of NOR-R-AM was a new graduate (Master's and PhD-level) course called Arctic Tectonics and Volcanism that we have established and taught annually at UNIS since 2018 and detail herein. The course's main objective is to teach the complex geological evolution of the Arctic from the Devonian period (∼ 420 million years ago, Ma) to the present day through integrating multi-scale datasets and a broad range of geoscientific disciplines. We outline the course itself before presenting student perspectives based on both an anonymous questionnaire (n=27) and in-depth perceptions of four selected students. The course, with an annual intake of up to 20 MSc and PhD students, is held over a 6-week period, typically in spring or autumn. The course comprises modules on field and polar safety, Svalbard/Barents Sea geology, wider Arctic geology, plate tectonics, mantle dynamics, geo- and thermochronology, and geochemistry of igneous systems. A field component, which in some years included an overnight expedition, provides an opportunity to appreciate Arctic geology and gather field observations and data. Digital outcrop models, photospheres, and tectonic plate reconstructions provide complementary state-of-the-art data visualization tools in the classroom and facilitate efficient fieldwork through pre-fieldwork preparation and post-fieldwork quantitative analyses. The course assessment is centred around an individual research project that is presented orally and in a short and impactful Geology journal-style article. Considering the complex subject and the diversity of students' backgrounds and level of geological knowledge before the course, the student experiences during this course demonstrate that the multi-disciplinary, multi-lecturer field-and-classroom teaching is efficient and increases their motivation to explore Arctic science.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".