Reintegrating Cultural and Natural Landscapes
Bibliographic record
Abstract
Landscapes are important frames for understanding and bridging environmental perspectives, including between Indigenous and scientific knowledge systems. Landscapes are both “natural” and “cultural,” for, as Indigenous societies attest, all landscapes manifest the coevolutionary interplay of human and nonhuman forces. We apply three integrated ecological lenses to analyze this interplay: historical ecology, ethno-ecology, and political ecology. Our case study is the Alsek-Dry Bay region of Southeast Alaska and Western Canada, at the intersection of the northern Tlingit and Athabaskan worlds. Historically an epicenter of astonishing geological dynamism and disruption, biological productivity and diversity, this landscape was also a mecca of cultural exchange, contestation, and appropriation. Ironically, the Alsek-Dry Bay landscape is now “preserved” as the center of a celebrated World Heritage Site based solely on its “natural” landscapes and “wilderness” character, and not for its Indigenous identity as a place of outstanding cultural significance – where the trickster-worldmaker Raven literally transformed the cosmos and topography – and the product of deep cultural-environmental histories. Bringing these ecological perspectives together enables a broader appreciation of the natural and cultural dynamism that has shaped such sites and of the enduring value and lessons of Indigenous knowledge systems that have coevolved with rapidly changing landscapes.
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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 | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".