Negotiation strategies and agreement-making models in large-scale resource development projects in Yamal, Arctic Russia
Bibliographic record
Abstract
Based on ethnographic fieldwork material and other data collected during 2006–2009, this article examines contemporary models of agreement-making and partnerships negotiated between the local communities, government, and resource corporations in the Russian District of Purovsky (Arctic Yamal). The Yamal region has Eurasia’s richest oil and gas reserves, and is an important crossroads region where various geopolitical and financial interests intersect. With the opening up of new gas and oil fields, and construction of roads and pipelines, Yamal is experiencing rapid changes and is being challenged to reshape its many ‘frontiers’ in which people, energy, and decisions are closely linked to one another. Since the late 1970s, resource development projects have had significant impacts on the lives of the local people in the Purovsky tundra. Along with experiencing negative consequences, such as water and soil contamination and other impacts on land, society and wildlife, local communities have also developed creative ways of adaptation, decision-making, and self-organization.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".