Learning the Yup’ik way of navigation: Studying time, position, and direction
Bibliographic record
Abstract
This paper is about the use of mathematics in Yup’ik navigation strategies, as practiced by Fred George of Akiachak, Alaska. Fred George travels by snow machine over snow covered frozen lakes and tundra in the Yukon-Kuskokwim Delta. In day light, he uses the position of the sun and time of day to determine his direction. On clear nights, he uses the position of the Big Dipper and time of night to determine his direction. In addition, he observes the frozen grass, isolated trees, and/or snow waves to reinforce his direction. The sun, Big Dipper, frozen grass, isolated trees, and snow waves function as natural compasses for Fred George. Originally mentored by his father when he was a boy, Fred has continued to develop his navigational skills on the tundra for over 60 years to hunt and fish for his family of eight children and many grandchildren. He passionately wants to pass his navigational skills on to the young people in Akiachak. He knows young people are no longer being mentored by their families to navigate. Yet many
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".