The Russian offshore oil and gas regime: when tight control means less order
Bibliographic record
Abstract
Global energy problems will remain a challenge in the coming decades. The impact of climate change and the melting of polar sea ice opening up access to offshore hydrocarbon resources in the Arctic Ocean, raises questions for both civil society and the scientific community over drilling opportunities in Arctic marine areas. \n \nDisparities in approach to the governance of oil and gas extraction in the Arctic arise from fundamental differences in histories, cultures, domestic constraints and substantive values and attitudes in the Arctic coastal states and sub-states. Differing political systems, legal traditions and societal beliefs with regard to energy security and economic development, environmental protection, legitimacy of decision making, and the ownership and respect of the rights of indigenous people, all affect how governance systems of oil and gas extraction are designed. \n \nUsing a multidisciplinary approach and case studies from the USA, Norway, Russia, Canada, Greenland/Denmark and the EU, this book both examines the current governance of extraction and its effects and considers ways to enhance the efficiency of environmental management and public participation in this system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".