MétaCan
Menu
Back to cohort

Arctic ice in cross-disciplinary undergraduate education: Experiences across natural science, social science, international policy, and public writing

2020· article· en· W3084181454 on OpenAlexaboutno aff
Michelle Koutnik, Nadine C. Fabbi, Elizabeth Wessells, Ellen A. Ahlness, Max Showalter, Dan Mandeville, Jason Young, Hans Christian Steen‐Larsen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsArcticDisciplinePermafrostHigher educationPolitical sciencePublic relationsSociologyOceanographySocial scienceGeology

Abstract

fetched live from OpenAlex

<p>With the Arctic currently warming at a rate at least twice that of the global average, the coupled Arctic ecosystem is losing ice. This includes significant land-ice loss from the Greenland Ice Sheet and Arctic ice caps and glaciers, reduction in extent and thickness of Arctic sea ice, and thawing permafrost. This scale of environmental change significantly affects Arctic people, wildlife, infrastructure, transportation, and access. Societal response to these changes relies on advances in and application of research spanning multiple scientific disciplines, with policy-making done in partnership with Indigenous people, governments, private agencies, multinational corporations, and other interested groups. Everyone will interface with outcomes due to a changing climate and the challenge is mounting for the next generation of leaders. The cross-disciplinary nature of the challenge of Arctic ice loss and climate change must be met by cross-disciplinary undergraduate education. While higher education aims for disciplinary training in natural sciences and social sciences, there is an increasing responsibility to integrate topics and immerse students in real-world issues. And, in our experience the undergraduates we teach are eager for courses that can do this well.</p><p>What is immersive undergraduate education? We consider this as either immersing students in a focused topic in the classroom, immersing students in a place (especially while abroad), or combining the two through targeted lectures, informed discussions, travel, and writing. With regard to the Arctic, it is necessary to bring scientific understanding to learning activities otherwise focused on societal impacts, policy making, and knowledge exchange through public writing.</p><p>We share from our practical experience teaching Arctic-focused courses to classes each with 10-30 students with majors from across the University of Washington (UW) campus (total undergraduate student body of 32,000). Three recent activities that integrate the state of science with impacts on society in undergraduate courses include: 1) a four-week study abroad course to Greenland and Denmark focusing on changes in the Greenland Ice Sheet and sea-level rise, 2) a 10-week Task Force course in Arctic Sea Ice and International Policy in partnership with the UW International Policy Institute at the Henry M. Jackson School of International Studies that includes one-week in Ottawa where students develop a mock Arctic sea ice policy for Canada consistent with Inuit priorities, and 3) a 10-week seminar in public writing where students write mock newspaper articles, book reviews, and policy summaries about ice in a changing climate. These courses were designed to include a similar subset of earth science, atmospheric science, and oceanography, but the distinct structure and application of the science in these three separate courses led to distinct learning outcomes. In addition, we present how the academic minor in Arctic Studies at the University of Washington has allowed students to design their own integrated understanding of Indigenous and nation-state Arctic geopolitics, Arctic environmental change, and policy by taking a selection of courses and engaging in research and report writing.</p>

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.011
Science and technology studies0.0030.005
Scholarly communication0.0070.006
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.094
GPT teacher head0.502
Teacher spread0.408 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicInterdisciplinary Research and CollaborationFrench-language works237,207