Mapping Our Return: Glacier Stories and Knowledge Production in Climate Change
Bibliographic record
Abstract
Over 250 years ago, a young Tlingit woman called to a glacier that displaced the Xunaa Tlingit and beckoned to the U.S. National Park Service.Today, in the midst of climate change, Glacier Bay National Park and Preserve is once again undergoing a huge transformation; glaciers are disappearing and the Xunaa Tlingit are back.In a historic collaboration, a tribal house, Xunaa Shuka Hit, was built in 2016, and has the potential to transform people, place and thought, that inform climate change solutions.Based on my positionality as Tlingit interpreter of Xunaa Shuka Hit and park ranger, my research aims to analyze the collaboration from my perspective in terms science and Tlingit art, stories, and names that reveal emergent knowledges and blur lines of division.New glacier stories and locations for interpretive opportunities emerge from putting into conversation key materials and moments such as the bones of a whale and the interior screen of the Xunaa Shuka Hit, seagull eggs and park brochure maps, and an unlikely relationship between Tlingit elder and park ranger that enliven concepts of listening, mobility, and kinship.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".