Museum cultural collections: pathways to the preservation of traditional and scientific knowledge
Bibliographic record
Abstract
Museums of natural and cultural history in the 21st century hold responsibilities that are vastly different from those of the 19th and early 20th centuries, the time of many of their inceptions. No longer conceived of as cabinets of curiosities, institutional priorities are in the process of undergoing dramatic changes. This article reviews the history of the University of Alaska Museum in Fairbanks, Alaska, from its development in the early 1920s, describing the changing ways staff have worked with Indigenous individuals and communities. Projects like the Modern Alaska Native Material Culture and the Barter Island Project are highlighted as examples of how artifacts and the people who constructed them are no longer viewed as simply examples of material culture and Native informants but are considered partners in the acquisition, preservation, and perpetuation of traditional and scientific knowledge in Alaska.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.025 | 0.013 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".