Associative cultural landscape approach to interpreting traditional ecological wisdom: A case of Inuit habitat
Bibliographic record
Abstract
Global climate change and the accelerated melting of glaciers have raised concerns about the ability to manage ice-snow environments. Historically, human ancestors have mastered the ecological wisdom of working with ice-snow environments, but the phenomenon has not yet been articulated in cultural landscape methodologies that emphasize “nature-culture relevance”. The challenging living environment often compels indigenous people to form a strong bond with their surroundings, leading to the creation of long-term ecological wisdom through synergistic relationships with the environment. This ecological environment is conceptualized as a cognitive space in the form of the landscape, with which the aboriginal community norms and individual spirits continually interact. Such interactions generate numerous non-material cultural evidences, such as culture, art, religion, and other ideological aspects of the nation. These evidences symbolize the intellectual outcome of the relationship between humans and the landscape, and they create the “spiritual relevance” through personification and contextualization. The aim of the study is to explore the traditional ecological wisdom of the Inuit people who live in the harsh Arctic, and analyze the Inuit's interaction with the landscape through the lens of “associative cultural landscape”, and decode the survival experience that the Inuit have accumulated through their long-term synergy with the Arctic environment. The findings focus on the synergy between the Inuit and the ice-snow landscape, examining the knowledge and ecological wisdom that the Inuit acquire from the ice-snow landscape. Our goal is to develop a perspective of the ecological environment from the viewpoint of aboriginal people and establish a methodology, model, and framework for “associative cultural landscape” that incorporates ethnic non-material cultural evidences. From the results, a total of nine models for interpreting traditional Inuit ecological wisdom are generated based on the “diamond model” of “associative cultural landscape”, covering the transition from the physical landscape to a spiritual one and demonstrating the associative role of the landscape in stimulating potential spiritual cognitive abilities in humans.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.024 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".