Arctic science education using public museum collections from the University of Alaska Museum: an evolving and expanding landscape
Bibliographic record
Abstract
Alaska faces unique challenges in STEM (science, technology, engineering, and mathematics) education, including limited accessibility to resources and learning opportunities, and a lack of place-based education resources. Museum education programs, traditionally focused on public outreach through docent-led tours, are playing an increasingly important role in both formal and informal aspects of STEM education to help address these challenges. The University of Alaska Museum (UAM) stands as a model in the Arctic region exemplifying how public natural history museum collections can be utilized to create active place-based learning experiences with the aim of increasing engagement in STEM literacy and building connections between museums and communities. These efforts take many forms, including the development of teaching materials involving physical objects and (or) online data from the open-access database ARCTOS, training preservice teachers, and implementing citizen science projects. Because many UAM specimens and objects are from Alaska, they are easily incorporated into place-based education, thereby demonstrating how the Arctic environment is unique at local and regional scales. Here, we showcase several programs that are either unique to UAM or part of larger national projects and include exemplar teaching modules in order to provide learning opportunities in the Arctic region and other rural settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".