Geographical Model of Organizing Ethno-Eco-Recreational Zones (Based on Field Research in the Territories of Residence of Autochthonous Peoples)
Bibliographic record
Abstract
The article attempts to develop a geographical model for organizing ethno-eco-recreational zones using individual territories of residence of autochthonous peoples of the Far North as an example for the purpose of its adaptation to Russian conditions and scaling in the regions of the Russian Arctic. The hypothesis of the study suggests the possibility of applying this model for managing ethno-eco-recreational zones within the Far North and the Arctic zone of the Russian Federation. The territory of the Canadian Arctic North, which is very similar in terms of natural, climatic, settlement, and socio-economic characteristics, is proposed as an analogue for the formation of a geographical model. When writing the article, the authors relied on their own expedition experience and on materials collected in the field: USA 2001 (territory of the state of Wisconsin, reservation of the Algonquin Indian people - Menominee); Canada 2017-2019 (territory of the province of Quebec, reservation of the Algonquin Indian people - Abenaki); Russian Arctic 2023–2024 (polar expeditions “NORDVIK 2023” and “Chistaya Arctic – East – 77”). The model was developed using collected data from sociological and field observations, analytical material.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".