Bibliographic record
Abstract
In northwestern North America, glaciers figure prominently in both indigenous oral traditions and narratives of geophysical sciences. These perspectives intersect in discussions about global warming, predicted to be extreme at Arctic and Subarctic latitudes and an area of concern for both local people and scientists. Indigenous people in northwestern North America have experienced climate variability associated with the latter phases of the Little Ice Age (approximately 1550-1850). This paper draws on oral traditions passed down from that period, some recorded between 1900 and the early 1950s in coastal Alaska Tlingit communities and others recorded more recently with elders from Yukon First Nations. The narratives concern human travel to the Gulf of Alaska foreshore at the end of the Little Ice Age from the Copper River, from the Alaska panhandle, and from the upper Alsek-Tatshenshini drainage, as well as observations about glacier advances, retreats, and surges. The paper addresses two large policy debates. One concerns the incorporation of local knowledge into scientific research. The second addresses the way in which oral tradition contributes another variety of historical understanding in areas of the world where written documents are relatively recent. Academic debates, whether in science or in history, too often evaluate local expertise as data or evidence, rather than as knowledge or theory that might contribute different perspectives to academic questions.
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 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.047 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".