Movement and Native American Landscapes: A Comparative Approach
Bibliographic record
Abstract
Landscapes are created by people through their experience and engagement with the world around them and through their activities and movements on the ground. Human groups humanize an environment by mapping themselves onto the landscape using their knowledge of specific landforms and waterways, resources including minerals, plants and animals, and human settlements. Once established, this human imprint transforms the natural landscape into a cultural landscape and establishes a pattern of land use which can persist for generations, if not millennia. The objective of this paper is to examine and compare native perceptions and uses of landscapes using historic maps, established travel and trade routes, and ethnographic data on settlement locations for groups occupying the boreal forest and northwesern Plains of Canada. The data indicate that native perceptions of the landscape are rooted in the landforms and vegetation present in an area as well as the transportation technology available to the group. Although movement and vegetation influence the selection of landmarks on the landscape, mythology and oral traditions describe the origin and spiritual relationships of features on the landscape.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".