Yellowknives Dene and Gwich’in Stellar Wayfinding in Large-Scale Subarctic Landscapes
Bibliographic record
Abstract
Indigenous systems of stellar wayfinding are rarely described or robustly attested outside of maritime contexts, with few examples reported among peoples of the high Arctic and some desert regions. However, like other large-scale environments that exhibit a low legibility of landmarks, the barrenlands of the Northwest Territories and the Yukon Flats of Alaska generally lack views of prominent or distinguishing topography for using classic route-based navigation. When travelling off trails and waterways in these respective inland subarctic environments, the Yellowknives Dene and the Alaskan Gwich’in utilize drastically different stellar wayfinding approaches from one another while essentially sharing the same view of the sky. However, in both systems the use of celestial schemata is suspended in favor of route-based navigation when the traveller intersects a familiar geographical feature or trail near their target destination, suggesting strong preference for orienting by landmarks when available. A comparison of both wayfinding systems suggests that large-scale environments that lack a readily discernible ground pattern may be more conducive to the development and implementation of a celestial wayfinding schema when combined with other influential factors such as culture, individual experience, and travel behavior. These are likely the first stellar wayfinding systems described in detail for any inland subarctic culture.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".